Lin, T.I.[Tsung I.],
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Elsevier DOI Bayesian classifier; Data augmentation; EM algorithm; Incomplete
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Elsevier DOI
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Latent variable support vector machine
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Pseudo feature representation, Zero-shot learning,
Supervised learning, Data augmentation, Attribute learning
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IEEE DOI
1904
Feature extraction, Training, Task analysis, Kernel, Face,
Adaptation models, Benchmark testing,
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Feature augmentation for imbalanced classification with conditional
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1906
Imbalanced classification, Feature augmentation,
Generative adversarial nets, Wasserstein distance
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Safe Classification with Augmented Features,
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IEEE DOI
1908
Support vector machines, Optimization,
Magnetic resonance imaging, Kernel, Testing, Data collection,
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1908
convolutional neural nets, entropy, feature extraction,
image annotation, image retrieval,
data augmentation
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Lucid Data Dreaming for Video Object Segmentation,
IJCV(127), No. 9, September 2019, pp. 1175-1197.
Springer DOI
1908
Generate in-domain training data using the provided annotation on the
first frame of each video.
BibRef
Yu, F.W.[Fei-Wu],
Wu, X.X.[Xin-Xiao],
Chen, J.L.[Jia-Lu],
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Exploiting Images for Video Recognition: Heterogeneous Feature
Augmentation via Symmetric Adversarial Learning,
IP(28), No. 11, November 2019, pp. 5308-5321.
IEEE DOI
1909
Training, Neural networks, Image recognition, Feature extraction,
Generative adversarial networks,
image-to-video adaptation
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Deng, T.[Ting],
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Generate adversarial examples by spatially perturbing on the
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PRL(125), 2019, pp. 632-638.
Elsevier DOI
1909
Adversarial attack, Spatial transformation, Grad-CAM
BibRef
Zheng, C.,
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Cui, J.,
Hyperspectral Image Classification With Small Training Sample Size
Using Superpixel-Guided Training Sample Enlargement,
GeoRS(57), No. 10, October 2019, pp. 7307-7316.
IEEE DOI
1910
feedforward neural nets, geophysical image processing,
hyperspectral imaging, image classification, image segmentation,
training sample enlargement
BibRef
Chiaroni, F.,
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Dufaux, F.,
Hallucinating A Cleanly Labeled Augmented Dataset from A Noisy
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ICIP19(3616-3620)
IEEE DOI
1910
Generative adversarial networks, noisy labeled learning, image classification
BibRef
Dupre, R.,
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Argyriou, V.,
Remagnino, P.,
Improving Dataset Volumes and Model Accuracy With Semi-Supervised
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IP(29), 2020, pp. 4337-4348.
IEEE DOI
2002
Training, Data models, Semisupervised learning, Task analysis,
Noise measurement, Deep learning, Solid modeling, Semi-supervised,
machine learning
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Beery, S.,
Liu, Y.,
Morris, D.,
Piavis, J.,
Kapoor, A.,
Meister, M.,
Joshi, N.,
Perona, P.,
Synthetic Examples Improve Generalization for Rare Classes,
WACV20(852-862)
IEEE DOI
2006
Data models, Training, Animals, Cameras, Training data,
Atmospheric modeling, Analytical models
BibRef
Song, L.,
Xu, Y.,
Zhang, L.,
Du, B.,
Zhang, Q.,
Wang, X.,
Learning From Synthetic Images via Active Pseudo-Labeling,
IP(29), 2020, pp. 6452-6465.
IEEE DOI
2007
Task analysis, Data models, Training, Visualization,
Adaptation models, Neural networks, Predictive models,
object detection
BibRef
Pan, X.J.[Xing-Jia],
Tang, F.[Fan],
Dong, W.M.[Wei-Ming],
Gu, Y.[Yang],
Song, Z.C.[Zhi-Chao],
Meng, Y.P.[Yi-Ping],
Xu, P.F.[Peng-Fei],
Deussen, O.[Oliver],
Xu, C.S.[Chang-Sheng],
Self-Supervised Feature Augmentation for Large Image Object Detection,
IP(29), 2020, pp. 6745-6758.
IEEE DOI
2007
Feature extraction, Object detection, Task analysis,
Image resolution, Pipelines, Convolution, Detectors, Self-supervise,
large image
BibRef
Takahashi, R.[Ryo],
Matsubara, T.[Takashi],
Uehara, K.[Kuniaki],
Data Augmentation Using Random Image Cropping and Patching for Deep
CNNs,
CirSysVideo(30), No. 9, September 2020, pp. 2917-2931.
IEEE DOI
2009
Training, Task analysis, Image color analysis,
Principal component analysis, Convolutional neural networks,
image-caption retrieval
BibRef
Naghizadeh, A.[Alireza],
Abavisani, M.[Mohammadsajad],
Metaxas, D.N.[Dimitris N.],
Greedy AutoAugment,
PRL(138), 2020, pp. 624-630.
Elsevier DOI
2010
AutoAugment, Augmentation, ANN, Neural networks, Vision, Classification
BibRef
Hu, C.[Cong],
Wu, X.J.[Xiao-Jun],
Li, Z.Y.[Zuo-Yong],
Generating adversarial examples with elastic-net regularized boundary
equilibrium generative adversarial network,
PRL(140), 2020, pp. 281-287.
Elsevier DOI
2012
Adversarial example, Elastic-net regularization,
Generative adversarial network, Semi-whitebox attack, Blackbox attack
BibRef
Cen, F.[Feng],
Zhao, X.Y.[Xiao-Yu],
Li, W.Z.[Wu-Zhuang],
Wang, G.H.[Guang-Hui],
Deep feature augmentation for occluded image classification,
PR(111), 2021, pp. 107737.
Elsevier DOI
2012
Deep feature augmentation, Image occlusion,
Convolutional neural networks, Image classification
BibRef
Zhang, X.,
Wang, Z.,
Liu, D.,
Lin, Q.,
Ling, Q.,
Deep Adversarial Data Augmentation for Extremely Low Data Regimes,
CirSysVideo(31), No. 1, January 2021, pp. 15-28.
IEEE DOI
2101
Training, Generative adversarial networks,
Data models, Task analysis, Semisupervised learning,
object detection
BibRef
Tran, N.T.,
Tran, V.H.,
Nguyen, N.B.,
Nguyen, T.K.,
Cheung, N.M.,
On Data Augmentation for GAN Training,
IP(30), 2021, pp. 1882-1897.
IEEE DOI
2101
Generative adversarial networks, Generators, Training,
Task analysis, Standards, Data models,
CycleGAN
BibRef
Adamiak, M.[Maciej],
Bedkowski, K.[Krzysztof],
Majchrowska, A.[Anna],
Aerial Imagery Feature Engineering Using Bidirectional Generative
Adversarial Networks: A Case Study of the Pilica River Region, Poland,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Wang, W.N.[Wen-Ning],
Liu, X.B.[Xue-Bin],
Mou, X.Q.[Xuan-Qin],
Data Augmentation and Spectral Structure Features for Limited Samples
Hyperspectral Classification,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link
2103
BibRef
Liu, E.[Erhu],
Huang, S.[Song],
Zong, C.[Cheng],
Zheng, C.Y.[Chang-You],
Yao, Y.M.[Yong-Ming],
Zhu, J.[Jing],
Tang, S.Q.[Shi-Qi],
Wang, Y.Q.[Yan-Qiu],
MTGAN: Extending Test Case set for Deep Learning Image Classifier,
IEICE(E104-D), No. 5, May 2021, pp. 709-722.
WWW Link.
2105
BibRef
Dvornik, N.[Nikita],
Mairal, J.[Julien],
Schmid, C.[Cordelia],
On the Importance of Visual Context for Data Augmentation in Scene
Understanding,
PAMI(43), No. 6, June 2021, pp. 2014-2028.
IEEE DOI
2106
BibRef
Earlier:
Modeling Visual Context Is Key to Augmenting Object Detection Datasets,
ECCV18(XII: 375-391).
Springer DOI
1810
Context modeling, Object detection, Image segmentation, Semantics,
Training, Visualization, Task analysis,
semantic segmentation
BibRef
Dvornik, N.[Nikita],
Shmelkov, K.,
Mairal, J.[Julien],
Schmid, C.[Cordelia],
BlitzNet: A Real-Time Deep Network for Scene Understanding,
ICCV17(4174-4182)
IEEE DOI
1802
image segmentation,
learning (artificial intelligence), object detection, Semantics
BibRef
Illarionova, S.[Svetlana],
Nesteruk, S.[Sergey],
Shadrin, D.[Dmitrii],
Igantiev, V.[Vladimir],
Pukalchik, M.[Maria],
Oseledets, I.[Ivan],
MixChannel: Advanced Augmentation for Multispectral Satellite Images,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link
2106
Augmentation for training.
BibRef
Xie, H.[Hao],
Chen, Y.S.[Yu-Shi],
Ghamisi, P.[Pedram],
Remote Sensing Image Scene Classification via Label Augmentation and
Intra-Class Constraint,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Peng, D.[Duo],
Lei, Y.J.[Yin-Jie],
Liu, L.Q.[Ling-Qiao],
Zhang, P.P.[Ping-Ping],
Liu, J.[Jun],
Global and Local Texture Randomization for Synthetic-to-Real Semantic
Segmentation,
IP(30), 2021, pp. 6594-6608.
IEEE DOI
2108
To train on synthetic data.
Painting, Training, Semantics, Image segmentation, Complexity theory,
Adaptation models, Task analysis, consistency regularization
BibRef
Wang, H.[Hao],
Wang, Q.L.[Qi-Long],
Zhang, H.Z.[Hong-Zhi],
Yang, J.[Jian],
Zuo, W.M.[Wang-Meng],
Constrained Online Cut-Paste for Object Detection,
CirSysVideo(31), No. 10, October 2021, pp. 4071-4083.
IEEE DOI
2110
Augment samples by pasting foreground objects on other backgrounds.
Switches, Training, Object detection, Detectors, Convergence,
Coherence, Visualization, Object detection, data augmentation,
sample diversity
BibRef
Yang, W.[Wonseok],
Nam, W.[Woochul],
Data synthesis method preserving correlation of features,
PR(122), 2022, pp. 108241.
Elsevier DOI
2112
Data synthesis, Correlation, Artificial dataset, Random noise
BibRef
Acción, Á.[Álvaro],
Argüello, F.[Francisco],
Heras, D.B.[Dora B.],
A New Multispectral Data Augmentation Technique Based on Data
Imputation,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Arantes, R.B.[Renato B.],
Vogiatzis, G.[George],
Faria, D.R.[Diego R.],
Learning an augmentation strategy for sparse datasets,
IVC(117), 2022, pp. 104338.
Elsevier DOI
2112
GAN, Data augmentation, Semantic segmentation
BibRef
Madhusudana, P.C.[Pavan C.],
Lee, S.J.[Seok-Jun],
Sheikh, H.R.[Hamid R.],
Revisiting Dead Leaves Model: Training With Synthetic Data,
SPLetters(29), 2022, pp. 209-213.
IEEE DOI
2202
Computational modeling,
Solid modeling, Estimation, Training, Cameras, Data models, stereo matching
BibRef
Kachuee, M.[Mohammad],
Karkkainen, K.[Kimmo],
Goldstein, O.[Orpaz],
Darabi, S.[Sajad],
Sarrafzadeh, M.[Majid],
Generative Imputation and Stochastic Prediction,
PAMI(44), No. 3, March 2022, pp. 1278-1288.
IEEE DOI
2202
Uncertainty, Generators, Stochastic processes, Training, Data models,
Task analysis, Generative adversarial networks, Missing data,
classification uncertainty
BibRef
Xue, Z.F.[Zhen-Feng],
Mao, W.J.[Wei-Jie],
Liu, Y.[Yong],
Image-level dataset synthesis with an end-to-end trainable framework,
IET-IPR(16), No. 8, 2022, pp. 2228-2239.
DOI Link
2205
BibRef
Zhang, H.[Huan],
Xu, Z.Y.[Zhi-Yi],
Han, X.L.[Xiao-Lin],
Sun, W.D.[Wei-Dong],
Data Augmentation Using Bitplane Information Recombination Model,
IP(31), 2022, pp. 3713-3725.
IEEE DOI
2206
Object detection, Data mining, Data models, Gray-scale, Birds,
Remote sensing, Task analysis, Data augmentation, image classification
BibRef
Ali, U.[Usman],
Esau, T.J.[Travis J.],
Farooque, A.A.[Aitazaz A.],
Zaman, Q.U.[Qamar U.],
Abbas, F.[Farhat],
Bilodeau, M.F.[Mathieu F.],
Limiting the Collection of Ground Truth Data for Land Use and Land
Cover Maps with Machine Learning Algorithms,
IJGI(11), No. 6, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Liu, J.L.[Jia-Lun],
Sun, Y.F.[Yi-Fan],
Xu, Y.J.[Yi-Jin],
Pei, H.B.[Hong-Bin],
Li, W.H.[Wen-Hui],
Feature Cloud: Improving Deep Visual Recognition with Probabilistic
Feature Augmentation,
CirSysVideo(32), No. 7, July 2022, pp. 4122-4137.
IEEE DOI
2207
Head, Task analysis, Visualization, Training, Probabilistic logic, Sun,
Statistical analysis, Long-tailed distribution,
deep visual recognition
BibRef
Xu, Q.[Qin],
Sun, Y.F.[Yi-Fan],
Li, Y.L.[Ya-Li],
Wang, S.J.[Sheng-Jin],
Attend and Align: Improving Deep Representations with Feature
Alignment Layer for Person Retrieval,
ICPR18(2148-2153)
IEEE DOI
1812
Training, Noise measurement, Task analysis, Pipelines,
Spatial resolution, Testing
BibRef
Dou, H.X.[Hong-Xia],
Lu, X.S.[Xing-Shun],
Wang, C.[Chao],
Shen, H.Z.[Hao-Zhen],
Zhuo, Y.W.[Yu-Wei],
Deng, L.J.[Liang-Jian],
PatchMask: A Data Augmentation Strategy with Gaussian Noise in
Hyperspectral Images,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Wang, S.Y.[Shu-Yu],
Li, W.G.[Wen-Gen],
Hou, S.[Siyun],
Guan, J.H.[Ji-Hong],
Yao, J.[Jiamin],
STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network
for Missing Value Imputation in Satellite Data,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Adhikari, R.[Ramesh],
Pokharel, S.[Suresh],
Performance Evaluation of Convolutional Neural Network Using Synthetic
Medical Data Augmentation Generated by GAN,
IJIG(23), No. 1 2023, pp. 2350002.
DOI Link
2302
BibRef
Chadebec, C.[Clément],
Thibeau-Sutre, E.[Elina],
Burgos, N.[Ninon],
Allassonnière, S.[Stéphanie],
Data Augmentation in High Dimensional Low Sample Size Setting Using a
Geometry-Based Variational Autoencoder,
PAMI(45), No. 3, March 2023, pp. 2879-2896.
IEEE DOI
2302
Data models, Measurement, Training, Magnetic resonance imaging,
Databases, Task analysis, Variational autoencoders,
latent space modeling
BibRef
Hao, X.J.[Xue-Jie],
Liu, L.[Lu],
Yang, R.J.[Rong-Jin],
Yin, L.Y.[Lize-Yan],
Zhang, L.[Le],
Li, X.H.[Xiu-Hong],
A Review of Data Augmentation Methods of Remote Sensing Image Target
Recognition,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
Survey, Data Augmentation.
BibRef
Tsaregorodtsev, A.[Alexander],
Belagiannis, V.[Vasileios],
ParticleAugment: Sampling-based data augmentation,
CVIU(228), 2023, pp. 103633.
Elsevier DOI
2302
Deep learning optimization, Automated augmentation,
Sequential Monte Carlo, Particle filters
BibRef
Xu, M.[Mingle],
Yoon, S.[Sook],
Fuentes, A.[Alvaro],
Park, D.S.[Dong Sun],
A Comprehensive Survey of Image Augmentation Techniques for Deep
Learning,
PR(137), 2023, pp. 109347.
Elsevier DOI
2302
Image augmentation, Deep learning, Image variation,
Vicinity distribution, Data augmentation, Computer vision
BibRef
Lv, J.[Jia],
Song, K.[Kaikai],
Ye, Q.[Qiang],
Tian, G.J.[Guang-Jian],
Semi-supervised node classification via fine-grained graph auxiliary
augmentation learning,
PR(137), 2023, pp. 109301.
Elsevier DOI
2302
Graph neural network, Node classification, Data augmentation, Auxiliary learning
BibRef
Xu, C.J.[Cheng-Jun],
Shu, J.Q.[Jing-Qian],
Zhu, G.B.[Guo-Bin],
Adversarial Remote Sensing Scene Classification Based on Lie Group
Feature Learning,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
Added Lie group to GAN for augmentation.
BibRef
Zhang, L.F.[Lin-Feng],
Ma, K.[Kaisheng],
A Good Data Augmentation Policy is not All You Need:
A Multi-Task Learning Perspective,
CirSysVideo(33), No. 5, May 2023, pp. 2190-2201.
IEEE DOI
2305
Training, Task analysis, Head, Data models, Search problems,
Optimization, Neural networks, Data augmentation, deep neural networks
BibRef
Xiao, A.[Anqi],
Shen, B.[Biluo],
Tian, J.[Jie],
Hu, Z.H.[Zhen-Hua],
Differentiable RandAugment: Learning Selecting Weights and Magnitude
Distributions of Image Transformations,
IP(32), 2023, pp. 2413-2427.
IEEE DOI
2305
Task analysis, Training, Data models, Costs, Optimization,
Search problems, Upper bound, Data augmentation, random augmentation
BibRef
Zhang, M.Y.[Ming-Yang],
Wang, Z.Y.[Zhao-Yang],
Wang, X.Y.[Xiang-Yu],
Gong, M.G.[Mao-Guo],
Wu, Y.[Yue],
Li, H.[Hao],
Features kept generative adversarial network data augmentation
strategy for hyperspectral image classification,
PR(142), 2023, pp. 109701.
Elsevier DOI
2307
Hyperspectral images (HSIs), Deep learning,
Generative adversarial network (GAN), Data augmentation
BibRef
Bai, Y.L.[Ya-Long],
Zhou, M.[Mohan],
Zhang, W.[Wei],
Zhou, B.[Bowen],
Mei, T.[Tao],
Augmentation Pathways Network for Visual Recognition,
PAMI(45), No. 8, August 2023, pp. 10580-10587.
IEEE DOI
2307
Standards, Training, Visualization, Network architecture,
Convolutional neural networks, Crops, Convolution,
visual recognition
BibRef
Agarwal, T.[Tushar],
Sugavanam, N.[Nithin],
Ertin, E.[Emre],
Sparse Signal Models for Data Augmentation in Deep Learning ATR,
RS(15), No. 16, 2023, pp. 4109.
DOI Link
2309
BibRef
Castellini, A.[Alberto],
Masillo, F.[Francesco],
Azzalini, D.[Davide],
Amigoni, F.[Francesco],
Farinelli, A.[Alessandro],
Adversarial Data Augmentation for HMM-Based Anomaly Detection,
PAMI(45), No. 12, December 2023, pp. 14131-14143.
IEEE DOI
2311
BibRef
Sun, R.[Rémy],
Masson, C.[Clément],
Hénaff, G.[Gilles],
Thome, N.[Nicolas],
Cord, M.[Matthieu],
Semantic augmentation by mixing contents for semi-supervised learning,
PR(145), 2024, pp. 109909.
Elsevier DOI
2311
Deep-learning, Semi-supervised learning, Data augmentation, Mixing augmentation
BibRef
Chen, D.[Dong],
Zhuang, Y.T.[Yue-Ting],
Shen, Z.J.[Zi-Jin],
Yang, C.[Carl],
Wang, G.M.[Guo-Ming],
Tang, S.L.[Si-Liang],
Yang, Y.[Yi],
Cross-Modal Data Augmentation for Tasks of Different Modalities,
MultMed(25), 2023, pp. 7814-7824.
IEEE DOI Code:
WWW Link.
2312
BibRef
Dornaika, F.[Fadi],
Sun, D.Y.[Dan-Yang],
LGCOAMix: Local and Global Context-and-Object-Part-Aware
Superpixel-Based Data Augmentation for Deep Visual Recognition,
IP(33), 2024, pp. 205-215.
IEEE DOI Code:
WWW Link.
2312
Data augmentationb beyond cut-and-paste.
BibRef
Yu, X.Y.[Xiao-Yu],
Li, F.C.[Fu-Chao],
Bai, P.F.[Peng-Fei],
Liu, Y.[Yan],
Chen, Y.[Yinglu],
Copy-paste with self-adaptation: A self-adaptive adjustment method
based on copy-paste augmentation,
IET-CV(17), No. 8, 2023, pp. 936-947.
DOI Link
2312
image enhancement, object detection, object recognition
BibRef
Yang, S.[Suorong],
Guo, S.[Suhan],
Zhao, J.[Jian],
Shen, F.[Furao],
Investigating the effectiveness of data augmentation from similarity
and diversity: An empirical study,
PR(148), 2024, pp. 110204.
Elsevier DOI
2402
Data augmentation, Interpretability, Generalization,
Deep learning, Image classification
BibRef
Lin, S.Q.[Shi-Qi],
Yu, T.[Tao],
Feng, R.[Ruoyu],
Li, X.[Xin],
Yu, X.Y.[Xiao-Yuan],
Xiao, L.[Lei],
Chen, Z.B.[Zhi-Bo],
Local Patch AutoAugment With Multi-Agent Collaboration,
MultMed(26), 2024, pp. 724-736.
IEEE DOI
2402
Reinforcement learning, Training, Task analysis, Semantics,
Deep learning, Image color analysis, Data models, reinforcement learning
BibRef
Georgiadis, C.[Charalampos],
Passalis, N.[Nikolaos],
Nikolaidis, N.[Nikos],
A synthetic human-centric dataset generation pipeline for active
robotic vision,
PRL(179), 2024, pp. 17-23.
Elsevier DOI
2403
Dynamically adjust view point.
Active robotic vision, Synthetic dataset, Dataset generation pipeline
BibRef
Ahn, N.[Namhyuk],
Yoo, J.J.[Jae-Jun],
Sohn, K.A.[Kyung-Ah],
Data Augmentation for Low-Level Vision:
CutBlur and Mixture-of-Augmentation,
IJCV(132), No. 6, June 2024, pp. 2041-2059.
Springer DOI
2406
Code:
WWW Link. Cut a patch and insert.
BibRef
Ofori-Oduro, M.[Mark],
Amer, M.A.[Maria A.],
Artificial immune systems for data augmentation,
IVC(149), 2024, pp. 105163.
Elsevier DOI Code:
WWW Link.
2408
BibRef
Earlier:
Data Augmentation Using Artificial Immune Systems For Noise-Robust
CNN Models,
ICIP20(833-837)
IEEE DOI
2011
Artificial immune systems, AIS, Data augmentation,
Image distortions, Object detection, Image classification, Object tracking.
Training, Data models, White noise, Artificial Immune Systems, AIS, CNN
BibRef
Chen, H.H.[Hong-Hui],
Zhao, B.Q.[Bao-Quan],
Yue, G.H.[Guang-Hui],
Liu, W.[Weide],
Lv, C.[Chenlei],
Wang, R.M.[Ruo-Mei],
Zhou, F.[Fan],
CLIP-Medfake: Synthetic Data Augmentation with AI-Generated Content
for Improved Medical Image Classification,
ICIP24(3854-3860)
IEEE DOI
2411
Training, Generative AI, Training data, Data augmentation,
Data models, Task analysis, Few shot learning,
artificial intelligence-generated content
BibRef
Ozturk, E.[Efe],
Prabhushankar, M.[Mohit],
AlRegib, G.[Ghassan],
Intelligent Multi-View Test Time Augmentation,
ICIP24(617-623)
IEEE DOI Code:
WWW Link.
2411
Measurement, Uncertainty, Accuracy, Neural networks,
Machine learning, Prediction algorithms, image classification,
robust machine learning
BibRef
Faghri, F.[Fartash],
Pouransari, H.[Hadi],
Mehta, S.[Sachin],
Farajtabar, M.[Mehrdad],
Farhadi, A.[Ali],
Rastegari, M.[Mohammad],
Tuzel, O.[Oncel],
Reinforce Data, Multiply Impact:
Improved Model Accuracy and Robustness with Dataset Reinforcement,
ICCV23(16986-16997)
IEEE DOI Code:
WWW Link.
2401
BibRef
Mohwald, A.[Albert],
Jenicek, T.[Tomas],
Chum, O.[Ondrej],
Dark Side Augmentation:
Generating Diverse Night Examples for Metric Learning,
ICCV23(11119-11129)
IEEE DOI Code:
WWW Link.
2401
BibRef
Hou, C.K.[Cheng-Kai],
Zhang, J.[Jieyu],
Zhou, T.Y.[Tian-Yi],
When to Learn What: Model-Adaptive Data Augmentation Curriculum,
ICCV23(1717-1728)
IEEE DOI Code:
WWW Link.
2401
BibRef
Liu, Y.[Yang],
Yan, S.[Shen],
Leal-Taixé, L.[Laura],
Hays, J.[James],
Ramanan, D.[Deva],
Soft Augmentation for Image Classification,
CVPR23(16241-16250)
IEEE DOI
2309
BibRef
Liu, H.T.[Hao-Tian],
Son, K.[Kilho],
Yang, J.W.[Jian-Wei],
Liu, C.[Ce],
Gao, J.F.[Jian-Feng],
Lee, Y.J.[Yong Jae],
Li, C.Y.[Chun-Yuan],
Learning Customized Visual Models with Retrieval-Augmented Knowledge,
CVPR23(15148-15158)
IEEE DOI
2309
BibRef
Yoshimura, M.[Masakazu],
Otsuka, J.J.[Jun-Ji],
Irie, A.[Atsushi],
Ohashi, T.[Takeshi],
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a
Wide Variety of Environments,
CVPR23(14007-14017)
IEEE DOI
2309
BibRef
Shen, Y.T.[Yi-Ting],
Lee, H.[Hyungtae],
Kwon, H.S.[Hee-Sung],
Bhattacharyya, S.S.[Shuvra S.],
Progressive Transformation Learning for Leveraging Virtual Images in
Training,
CVPR23(835-844)
IEEE DOI
2309
BibRef
Tripathi, A.[Aditay],
Singh, R.[Rishubh],
Chakraborty, A.[Anirban],
Shenoy, P.[Pradeep],
Edges to Shapes to Concepts: Adversarial Augmentation for Robust
Vision,
CVPR23(24470-24479)
IEEE DOI
2309
BibRef
Li, L.[Lujun],
Li, A.G.[Ang-Geng],
A2-Aug: Adaptive Automated Data Augmentation,
NAS23(2267-2274)
IEEE DOI
2309
BibRef
Tetková, L.[Lenka],
Hansen, L.K.[Lars Kai],
Robustness of Visual Explanations to Common Data Augmentation Methods,
XAI4CV23(3715-3720)
IEEE DOI
2309
BibRef
Zhang, L.R.[Ling-Rui],
Zhang, S.H.[Shu-Heng],
Xie, G.Y.[Guo-Yang],
Liu, J.Q.[Jia-Qi],
Yan, H.[Hua],
Wang, J.B.[Jin-Bao],
Zheng, F.[Feng],
Jin, Y.C.[Yao-Chu],
What Makes a Good Data Augmentation for Few-Shot Unsupervised Image
Anomaly Detection?,
VISION23(4345-4354)
IEEE DOI
2309
BibRef
Sreenivas, M.[Manogna],
Biswas, S.[Soma],
Similar Class Style Augmentation for Efficient Cross-Domain Few-Shot
Learning,
ECV23(4590-4598)
IEEE DOI
2309
BibRef
Khmaissia, F.[Fadoua],
Frigui, H.[Hichem],
Improving Automatic Target Recognition in Low Data Regime using
Semi-Supervised Learning and Generative Data Augmentation,
L3D-IVU23(4931-4939)
IEEE DOI
2309
BibRef
Singla, S.[Sumedha],
Murali, N.[Nihal],
Arabshahi, F.[Forough],
Triantafyllou, S.[Sofia],
Batmanghelich, K.[Kayhan],
Augmentation by Counterfactual Explanation:
Fixing an Overconfident Classifier,
WACV23(4709-4719)
IEEE DOI
2302
Training, Uncertainty, Measurement uncertainty, Medical services,
Robustness, Data models, Algorithms: Explainable, fair, accountable,
adversarial attack and defense methods
BibRef
Lee, S.[Saehyung],
Lee, H.[Hyungyu],
Inducing Data Amplification Using Auxiliary Datasets in Adversarial
Training,
WACV23(4540-4549)
IEEE DOI
2302
Training, Codes, Computational modeling, Neural networks, Training data,
Robustness,
Algorithms: Adversarial learning, adversarial attack and defense methods
BibRef
Engilberge, M.[Martin],
Shi, H.X.[Hai-Xin],
Wang, Z.Y.[Zhi-Ye],
Fua, P.[Pascal],
Two-level Data Augmentation for Calibrated Multi-view Detection,
WACV23(128-136)
IEEE DOI
2302
Computational modeling, Pipelines, Data models,
Algorithms: Image recognition and understanding, object detection
BibRef
Yu, R.R.[Rui-Ran],
Xu, Y.H.[Yu-Han],
Peng, H.[Haowei],
Chen, M.H.[Meng-Hui],
Enhancing Population Diversity by Integrating Iterative Local Search
with Deep Convolutional Generative Adversarial Networks (GANs),
ICPR22(4722-4728)
IEEE DOI
2212
Training, Sociology, Evolutionary computation,
Generative adversarial networks, Search problems, local search
BibRef
Tokieda, K.[Kodai],
Iwaguchi, T.[Takafumi],
Kawasaki, H.[Hiroshi],
Auto-Augmentation with Differentiable Renderer for High-frequency
Shape Recovery,
ICPR22(3952-3958)
IEEE DOI
2212
Training, Deep learning, Shape,
Superresolution, Estimation, Rendering (computer graphics)
BibRef
Körschens, M.[Matthias],
Bodesheim, P.[Paul],
Denzler, J.[Joachim],
Occlusion-Robustness of Convolutional Neural Networks via Inverted
Cutout,
ICPR22(2829-2835)
IEEE DOI
2212
Integrated circuits, Training,
Convolutional neural networks, Reliability, Task analysis
BibRef
Deschamps, S.[Sebastien],
Sahbi, H.[Hichem],
Reinforcement-based Display Selection for Frugal Learning,
ICPR22(1186-1193)
IEEE DOI
2212
Training, Adaptation models, Uncertainty, Self-supervised learning,
Linear programming, Probabilistic logic, Data models
BibRef
Sun, R.[Rémy],
Masson, C.[Clément],
Hénaff, G.[Gilles],
Thome, N.[Nicolas],
Cord, M.[Matthieu],
Swapping Semantic Contents for Mixing Images,
ICPR22(1280-1286)
IEEE DOI
2212
Training, Codes, Image synthesis, Semantics, Supervised learning,
Deep architecture, Semisupervised learning
BibRef
Yu, H.[Hong],
Li, F.Z.[Fan-Zhang],
Prototype Augmentation with Dummy Samples,
ICPR22(5052-5059)
IEEE DOI
2212
Training, Image color analysis, Prototypes, Jitter,
Feature extraction, Data mining, Task analysis
BibRef
Al-Afandi, J.[Jalal],
Magyar, B.[Bálint],
Horváth, A.[András],
Saliency Map Based Data Augmentation,
ICPR22(4751-4757)
IEEE DOI
2212
Training, Neural networks, Network architecture, Task analysis
BibRef
Duboudin, T.[Thomas],
Dellandréa, E.[Emmanuel],
Abgrall, C.[Corentin],
Hénaff, G.[Gilles],
Chen, L.M.[Li-Ming],
Look Beyond Bias with Entropic Adversarial Data Augmentation,
ICPR22(2142-2148)
IEEE DOI
2212
Training, Deep learning, Image recognition, Neural networks,
Process control, Benchmark testing, Entropy
BibRef
Zanella, R.H.[Rodrigo H.],
de Castro-Coelho, L.A.[Lucas A.],
Souza, V.M.A.[Vinicius M. A.],
TS-DENSE: Time Series Data Augmentation by Subclass Clustering,
ICPR22(1800-1806)
IEEE DOI
2212
Training, Image sensors, Insects, Time series analysis,
Predictive models, Sensors, Labeling
BibRef
Liu, Z.C.[Zi-Cheng],
Li, S.Y.[Si-Yuan],
Wu, D.[Di],
Liu, Z.H.[Zi-Han],
Chen, Z.Y.[Zhi-Yuan],
Wu, L.R.[Li-Rong],
Li, S.Z.[Stan Z.],
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers,
ECCV22(XXIV:441-458).
Springer DOI
2211
BibRef
Zhao, M.J.[Ming-Jun],
Lu, S.[Shan],
Wang, Z.X.[Zi-Xuan],
Wang, X.L.[Xiao-Li],
Niu, D.[Di],
LA3: Efficient Label-Aware AutoAugment,
ECCV22(XXI:262-277).
Springer DOI
2211
BibRef
Yan, B.M.[Bao-Ming],
Gao, K.[Ke],
Gao, B.[Bo],
Wang, L.[Lin],
Yang, J.[Jiang],
Li, X.B.[Xiao-Bo],
Unbiased Manifold Augmentation for Coarse Class Subdivision,
ECCV22(XXV:484-499).
Springer DOI
2211
BibRef
Liu, J.[Jihao],
Liu, B.[Boxiao],
Zhou, H.[Hang],
Li, H.S.[Hong-Sheng],
Liu, Y.[Yu],
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision
Transformers,
ECCV22(XXVI:455-471).
Springer DOI
2211
BibRef
Jean, R.[Raphaël],
St-Charles, P.L.[Pierre-Luc],
Pirk, S.[Sören],
Brodeur, S.[Simon],
Self-Supervised Learning of Pose-Informed Latents,
L3D-IVU22(4006-4015)
IEEE DOI
2210
Training, Visualization, Pose estimation, Video sequences,
Self-supervised learning, Benchmark testing
BibRef
Papakipos, Z.[Zoë],
Bitton, J.[Joanna],
AugLy: Data Augmentations for Adversarial Robustness,
ArtOfRobust22(155-162)
IEEE DOI
2210
Training, Time-frequency analysis, Image color analysis,
Social networking (online), Computational modeling, Robustness
BibRef
Kar, O.F.[Oguzhan Fatih],
Yeo, T.[Teresa],
Atanov, A.[Andrei],
Zamir, A.[Amir],
3D Common Corruptions and Data Augmentation,
CVPR22(18941-18952)
IEEE DOI
2210
Training, Image analysis, Computational modeling, Semantics,
Neural networks, Data models, Datasets and evaluation,
Scene analysis and understanding
BibRef
Bai, Y.[Yalong],
Yang, Y.F.[Yi-Fan],
Zhang, W.[Wei],
Mei, T.[Tao],
Directional Self-supervised Learning for Heavy Image Augmentations,
CVPR22(16671-16680)
IEEE DOI
2210
Codes, Self-supervised learning, Image representation,
Linear programming, Standards, retrieval
BibRef
Suzuki, T.[Teppei],
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge,
CVPR22(10894-10904)
IEEE DOI
2210
Training, Representation learning, Gradient methods,
Image color analysis, Computational modeling, Neural networks, retrieval
BibRef
Sattarzadeh, S.[Sam],
Shalmani, S.M.[Shervin Manzuri],
Azad, S.[Shervin],
Mitigating Paucity of Data in Sinusoid Characterization Using
Generative Synthetic Noise,
VDU22(4839-4848)
IEEE DOI
2210
Training, Deep learning, Image segmentation, Costs,
Computational modeling, Data acquisition
BibRef
Lee, S.[Sangjun],
Hwang, I.[Inwoo],
Kang, G.C.[Gi-Cheon],
Zhang, B.T.[Byoung-Tak],
Improving Robustness to Texture Bias via Shape-focused Augmentation,
HCIS22(4322-4330)
IEEE DOI
2210
Training, Visualization, Image resolution, Shape,
Supervised learning, Neural networks, Self-supervised learning
BibRef
Wen, H.[Hui],
Wu, Y.[Yue],
Li, J.J.[Jing-Jing],
Duan, H.C.[Han-Cong],
Communication-Efficient Federated Data Augmentation on Non-IID Data,
FedVision22(3376-3385)
IEEE DOI
2210
Wireless communication, Data privacy, Privacy,
Distributed databases, Machine learning, Collaborative work, Data models
BibRef
Liu, X.C.[Xiao-Chang],
Yang, Y.L.[Yong-Liang],
Hall, P.[Peter],
Geometric and Textural Augmentation for Domain Gap Reduction,
CVPR22(14320-14330)
IEEE DOI
2210
Geometry, Training, Visualization, Shape, Training data, Robustness,
Recognition: detection, categorization,
Machine learning
BibRef
Singh, V.V.[Vinit Veerendraveer],
Kambhamettu, C.[Chandra],
AIM: an Auto-Augmenter for Images and Meshes,
CVPR22(712-721)
IEEE DOI
2210
Deep learning, Training, Visualization, Image recognition,
Neural networks, Robustness, Visual reasoning
BibRef
Prakash, A.[Aayush],
Debnath, S.[Shoubhik],
Lafleche, J.F.[Jean-Francois],
Cameracci, E.[Eric],
State, G.[Gavriel],
Birchfield, S.[Stan],
Law, M.T.[Marc T.],
Self-Supervised Real-to-Sim Scene Generation,
ICCV21(16024-16034)
IEEE DOI
2203
Training on synthetic data.
Bridges, Training, Deep learning, Annotations, Scalability,
Neural networks, Vision for robotics and autonomous vehicles,
Scene analysis and understanding
BibRef
Liu, A.[Aoming],
Huang, Z.[Zehao],
Huang, Z.W.[Zhi-Wu],
Wang, N.Y.[Nai-Yan],
Direct Differentiable Augmentation Search,
ICCV21(12199-12208)
IEEE DOI
2203
WWW Link. Evaluate whether and how to augment.
Training, Neural networks, Graphics processing units,
Object detection, Search problems, Task analysis,
Recognition and classification
BibRef
Müller, S.G.[Samuel G.],
Hutter, F.[Frank],
TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation,
ICCV21(754-762)
IEEE DOI
2203
Costs, Codes, Reproducibility of results, Proposals, Task analysis,
Best practices, Recognition and classification, Datasets and evaluation
BibRef
Liu, Z.R.[Zi-Rui],
Jin, H.F.[Hai-Feng],
Wang, T.H.[Ting-Hsiang],
Zhou, K.X.[Kai-Xiong],
Hu, X.[Xia],
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity
Maximization,
ICCV21(4742-4750)
IEEE DOI
2203
Training, Bridges, Codes, Performance gain, Semisupervised learning,
Task analysis, Efficient training and inference methods,
Representation learning
BibRef
Sun, C.[Chen],
Nagrani, A.[Arsha],
Tian, Y.L.[Yong-Long],
Schmid, C.[Cordelia],
Composable Augmentation Encoding for Video Representation Learning,
ICCV21(8814-8824)
IEEE DOI
2203
Representation learning, Benchmark testing, Transformers, Encoding,
Data models, Task analysis,
Video analysis and understanding
BibRef
Patrick, D.[David],
Geyer, M.[Michael],
Tran, R.[Richard],
Fernandez, A.[Amanda],
Reconstructive Training for Real-World Robustness in Image
Classification,
Novelty22(251-260)
IEEE DOI
2202
Training, Measurement, Visualization, Data models,
Robustness, Sensors
BibRef
Hataya, R.[Ryuichiro],
Zdenek, J.[Jan],
Yoshizoe, K.[Kazuki],
Nakayama, H.[Hideki],
Meta Approach to Data Augmentation Optimization,
WACV22(3535-3544)
IEEE DOI
2202
Training, Representation learning, Visualization,
Image recognition, Semisupervised learning, Data models,
Deep Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Atienza, R.[Rowel],
Improving Model Generalization by Agreement of Learned
Representations from Data Augmentation,
WACV22(3927-3936)
IEEE DOI
2202
Training, Smoothing methods,
Computational modeling, Object detection, Predictive models,
Object Detection/Recognition/Categorization Data augmentation
BibRef
Nekrasov, A.[Alexey],
Schult, J.[Jonas],
Litany, O.[Or],
Leibe, B.[Bastian],
Engelmann, F.[Francis],
Mix3D: Out-of-Context Data Augmentation for 3D Scenes,
3DV21(116-125)
IEEE DOI
2201
Training, Geometry, Solid modeling, Codes, Semantics,
Benchmark testing, 3D scene understanding, 3D data augmentation
BibRef
Shibata, T.[Takashi],
Tanaka, M.[Masayuki],
Okutomi, M.[Masatoshi],
Geometric Data Augmentation Based on Feature Map Ensemble,
ICIP21(904-908)
IEEE DOI
2201
Image processing, Robustness, Task analysis,
Standards, data augmentation, test-time augmentation,
feature-map ensemble
BibRef
Cohen, J.[Julia],
Crispim-Junior, C.[Carlos],
Chiappa, J.M.[Jean-Marc],
Tougne, L.[Laure],
Training An Embedded Object Detector for Industrial Settings Without
Real Images,
ICIP21(714-718)
IEEE DOI
2201
Training, Performance evaluation, Solid modeling, Detectors,
Feature extraction, Data models, Object detection, Mobile applications
BibRef
Arar, M.[Moab],
Shamir, A.[Ariel],
Bermano, A.[Amit],
InAugment: Improving Classifiers via Internal Augmentation,
ILDAV21(1698-1707)
IEEE DOI
2112
Training, Image color analysis,
Computational modeling, Distortion
BibRef
Pennisi, M.[Matteo],
Palazzo, S.[Simone],
Spampinato, C.[Concetto],
Self-improving classification performance through GAN distillation,
ILDAV21(1640-1648)
IEEE DOI
2112
Training, Deep learning, Data visualization,
Generative adversarial networks, Feature extraction, Data models
BibRef
Zhang, Y.X.[Yu-Xuan],
Ling, H.[Huan],
Gao, J.[Jun],
Yin, K.X.[Kang-Xue],
Lafleche, J.F.[Jean-Francois],
Barriuso, A.[Adela],
Torralba, A.B.[Antonio B.],
Fidler, S.[Sanja],
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort,
CVPR21(10140-10150)
IEEE DOI
2111
Training, Image segmentation, Semantics,
Semisupervised learning, Generative adversarial networks
BibRef
Gong, C.Y.[Cheng-Yue],
Ren, T.Z.[Tong-Zheng],
Ye, M.[Mao],
Liu, Q.[Qiang],
MaxUp: Lightweight Adversarial Training with Data Augmentation
Improves Neural Network Training,
CVPR21(2474-2483)
IEEE DOI
2111
Deep learning, Perturbation methods,
Neural networks, Transforms, Robustness, Data models
BibRef
Nishi, K.[Kento],
Ding, Y.[Yi],
Rich, A.[Alex],
Höllerer, T.[Tobias],
Augmentation Strategies for Learning with Noisy Labels,
CVPR21(8018-8027)
IEEE DOI
2111
Training, Deep learning, Machine learning algorithms, Filtering,
Neural networks, Noise measurement
BibRef
Li, B.[Boyi],
Wu, F.[Felix],
Lim, S.N.[Ser-Nam],
Belongie, S.[Serge],
Weinberger, K.Q.[Kilian Q.],
On Feature Normalization and Data Augmentation,
CVPR21(12378-12387)
IEEE DOI
2111
Training, Image recognition, Shape, Image synthesis, Instruments,
Feature extraction, Data models
BibRef
Hong, M.[Minui],
Choi, J.[Jinwoo],
Kim, G.[Gunhee],
StyleMix: Separating Content and Style for Enhanced Data Augmentation,
CVPR21(14857-14865)
IEEE DOI
2111
Training, Deep learning, Head, Image color analysis,
Skin, Robustness
BibRef
Gudovskiy, D.[Denis],
Rigazio, L.[Luca],
Ishizaka, S.[Shun],
Kozuka, K.[Kazuki],
Tsukizawa, S.[Sotaro],
AutoDO: Robust AutoAugment for Biased Data with Label Noise via
Scalable Probabilistic Implicit Differentiation,
CVPR21(16596-16605)
IEEE DOI
2111
Deep learning, Probabilistic logic, Distortion,
Data models, Complexity theory
BibRef
Gong, C.Y.[Cheng-Yue],
Wang, D.[Dilin],
Li, M.[Meng],
Chandra, V.[Vikas],
Liu, Q.[Qiang],
KeepAugment:
A Simple Information-Preserving Data Augmentation Approach,
CVPR21(1055-1064)
IEEE DOI
2111
Training, Deep learning, Art, Object detection,
Boosting
BibRef
Dabouei, A.[Ali],
Soleymani, S.[Sobhan],
Taherkhani, F.[Fariborz],
Nasrabadi, N.M.[Nasser M.],
SuperMix: Supervising the Mixing Data Augmentation,
CVPR21(13789-13798)
IEEE DOI
2111
Training, Visualization, Codes,
Computational modeling, Classification algorithms
BibRef
Maeda, T.[Takahiro],
Ukita, N.[Norimichi],
Data Augmentation for Human Motion Prediction,
MVA21(1-5)
DOI Link
2109
Training, Data acquisition, Humanoid robots,
Kinematics, Animation, Data models
BibRef
Yang, Y.D.[Yan-Dan],
Sheng, L.[Lu],
Jiang, X.L.[Xiao-Long],
Wang, H.C.[Hao-Chen],
Xu, D.[Dong],
Cao, X.B.[Xian-Bin],
IncreACO: Incrementally Learned Automatic Check-out with
Photorealistic Exemplar Augmentation,
WACV21(626-634)
IEEE DOI
2106
Training, Computational modeling,
Layout, Pipelines, Data models
BibRef
Uricár, M.[Michal],
Sistu, G.[Ganesh],
Rashed, H.[Hazem],
Vobecký, A.[Antonín],
Kumar, V.R.[Varun Ravi],
Krížek, P.[Pavel],
Bürger, F.[Fabian],
Yogamani, S.[Senthil],
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling
Detection in Autonomous Driving,
WACV21(766-775)
IEEE DOI
2106
Degradation, Image segmentation, Semantics, Pipelines, Cameras,
Generative adversarial networks, Task analysis
BibRef
Olsson, V.[Viktor],
Tranheden, W.[Wilhelm],
Pinto, J.[Juliano],
Svensson, L.[Lennart],
ClassMix:
Segmentation-Based Data Augmentation for Semi-Supervised Learning,
WACV21(1368-1377)
IEEE DOI
2106
Training, Image segmentation,
Semantics, Manuals, Semisupervised learning
BibRef
Mounsaveng, S.[Saypraseuth],
Laradji, I.[Issam],
Ben Ayed, I.[Ismail],
Vázquez, D.[David],
Pedersoli, M.[Marco],
Learning Data Augmentation with Online Bilevel Optimization for Image
Classification,
WACV21(1690-1699)
IEEE DOI
2106
Training, Learning systems,
Computational modeling, Machine learning, Data models
BibRef
Inoue, N.[Nakamasa],
Yamagata, E.[Eisuke],
Kataoka, H.[Hirokatsu],
Initialization Using Perlin Noise for Training Networks with a
Limited Amount of Data,
ICPR21(1023-1028)
IEEE DOI
2105
Training, Knowledge engineering,
Complexity theory, Colored noise, Optimization, Image classification
BibRef
Suri, B.S.H.[Bhasker Sri Harsha],
Yeturu, K.[Kalidas],
Pseudo Rehearsal using non photo-realistic images,
ICPR21(4797-4804)
IEEE DOI
2105
Neural networks, Memory management, Training data,
Task analysis, Image classification
BibRef
Hegde, G.[Guruprasad],
Ramesh, A.N.[Avinash Nittur],
Gandikota, K.V.[Kanchana Vaishnavi],
Obermaisser, R.[Roman],
Moeller, M.[Michael],
A Simple Domain Shifting Network for Generating Low Quality Images,
ICPR21(3963-3968)
IEEE DOI
2105
Image quality, Deep learning, Image recognition,
Robot vision systems, Training data, Cameras
BibRef
Patel, K.[Kanil],
Beluch, W.[William],
Zhang, D.[Dan],
Pfeiffer, M.[Michael],
Yang, B.[Bin],
On-manifold Adversarial Data Augmentation Improves Uncertainty
Calibration,
ICPR21(8029-8036)
IEEE DOI
2105
Manifolds, Training, Uncertainty, Temperature, Estimation,
Stochastic processes, Network architecture
BibRef
Yang, H.[Hao],
Zhou, Y.[Yun],
IDA-GAN: A Novel Imbalanced Data Augmentation GAN,
ICPR21(8299-8305)
IEEE DOI
2105
Training, Measurement, Network intrusion detection,
Benchmark testing, Tools, Generative adversarial networks,
GAN
BibRef
Ma, W.X.[Wen-Xin],
Chen, J.[Jian],
Du, Q.[Qing],
Jia, W.[Wei],
PointDrop: Improving Object Detection from Sparse Point Clouds via
Adversarial Data Augmentation,
ICPR21(10004-10009)
IEEE DOI
2105
Training, Reflectivity, Solid modeling,
Detectors, Object detection, Robustness, 3D object detection,
adversarial learning
BibRef
Sasaki, H.[Hiroshi],
Willcocks, C.G.[Chris G.],
Breckon, T.P.[Toby P.],
Data Augmentation via Mixed Class Interpolation using
Cycle-Consistent Generative Adversarial Networks Applied to
Cross-Domain Imagery,
ICPR21(5083-5090)
IEEE DOI
2105
Night vision, Interpolation, Surveillance, Focusing,
Object detection, Security
BibRef
Hung, S.K.[Shih-Kai],
Gan, J.Q.[John Q.],
Augmentation of Small Training Data Using GANs for Enhancing the
Performance of Image Classification,
ICPR21(3350-3356)
IEEE DOI
2105
Training, Neural networks, Training data, Machine learning,
Generative adversarial networks, Robustness,
GANs
BibRef
Iwana, B.K.[Brian Kenji],
Uchida, S.[Seiichi],
Time Series Data Augmentation for Neural Networks by Time Warping
with a Discriminative Teacher,
ICPR21(3558-3565)
IEEE DOI
2105
Recurrent neural networks, Shape, Time series analysis, Tools,
Data models, Convolutional neural networks, Pattern matching
BibRef
Heindl, C.[Christoph],
Brunner, L.[Lukas],
Zambal, S.[Sebastian],
Scharinger, J.[Josef],
Blendtorch: A Real-time, Adaptive Domain Randomization Library,
IML20(538-551).
Springer DOI
2103
To create synthetic training data.
BibRef
Arantes, R.B.[Renato B.],
Vogiatzis, G.[George],
Faria, D.R.[Diego R.],
CSC-GAN: Cycle and Semantic Consistency for Dataset Augmentation,
ISVC20(I:170-181).
Springer DOI
2103
BibRef
He, Z.W.[Ze-Wen],
Wu, R.[Rui],
Zhang, D.Q.[Ding-Qian],
Cog: Consistent Data Augmentation for Object Perception,
ACCV20(III:143-154).
Springer DOI
2103
BibRef
Kozerawski, J.[Jedrzej],
Fragoso, V.[Victor],
Karianakis, N.[Nikolaos],
Mittal, G.[Gaurav],
Turk, M.[Matthew],
Chen, M.[Mei],
BLT: Balancing Long-tailed Datasets with Adversarially-perturbed Images,
ACCV20(III:338-355).
Springer DOI
2103
BibRef
Ammar, S.[Sirine],
Bouwmans, T.[Thierry],
Zaghden, N.[Nizar],
Neji, M.[Mahmoud],
Towards an Effective Approach for Face Recognition with DCGANS Data
Augmentation,
ISVC20(I:463-475).
Springer DOI
2103
BibRef
Le, H.,
Nguyen, M.,
Yan, W.Q.,
Machine Learning with Synthetic Data: A New Way to Learn and
Classify the Pictorial Augmented Reality Markers in Real-Time,
IVCNZ20(1-6)
IEEE DOI
2012
Training, Visualization, Video sequences, Machine learning,
Real-time systems, Task analysis, Augmented reality,
synthetic data generated
BibRef
Lou, L.,
Zhang, S.,
Zhang, S.,
Object Detection with the High-frequency Change of Objects Classes,
ISPRS20(B3:125-130).
DOI Link
2012
BibRef
Kuo, C.W.[Chia-Wen],
Ma, C.Y.[Chih-Yao],
Huang, J.B.[Jia-Bin],
Kira, Z.[Zsolt],
Featmatch: Feature-based Augmentation for Semi-supervised Learning,
ECCV20(XVIII:479-495).
Springer DOI
2012
BibRef
Chen, Y.L.[Yun-Lu],
Hu, V.T.[Vincent Tao],
Gavves, E.[Efstratios],
Mensink, T.[Thomas],
Mettes, P.S.[Pascal S.],
Yang, P.W.[Peng-Wan],
Snoek, C.G.M.[Cees G. M.],
PointMixup: Augmentation for Point Clouds,
ECCV20(III:330-345).
Springer DOI
2012
interpolation between examples.
BibRef
Hataya, R.[Ryuichiro],
Zdenek, J.[Jan],
Yoshizoe, K.[Kazuki],
Nakayama, H.[Hideki],
Faster Autoaugment: Learning Augmentation Strategies Using
Backpropagation,
ECCV20(XXV:1-16).
Springer DOI
2011
BibRef
Zou, J.H.[Jun-Hua],
Pan, Z.S.[Zhi-Song],
Qiu, J.Y.[Jun-Yang],
Liu, X.[Xin],
Rui, T.[Ting],
Li, W.[Wei],
Improving the Transferability of Adversarial Examples with
Resized-diverse-inputs, Diversity-ensemble and Region Fitting,
ECCV20(XXII:563-579).
Springer DOI
2011
BibRef
Yu, N.[Ning],
Li, K.[Ke],
Zhou, P.[Peng],
Malik, J.[Jitendra],
Davis, L.S.[Larry S.],
Fritz, M.[Mario],
Inclusive GAN:
Improving Data and Minority Coverage in Generative Models,
ECCV20(XXII:377-393).
Springer DOI
2011
BibRef
Li, Y.G.[Yong-Gang],
Hu, G.S.[Guo-Sheng],
Wang, Y.T.[Yong-Tao],
Hospedales, T.M.[Timothy M.],
Robertson, N.M.[Neil M.],
Yang, Y.X.[Yong-Xin],
Differentiable Automatic Data Augmentation,
ECCV20(XXII:580-595).
Springer DOI
2011
BibRef
Zoph, B.[Barret],
Cubuk, E.D.[Ekin D.],
Ghiasi, G.[Golnaz],
Lin, T.Y.[Tsung-Yi],
Shlens, J.[Jonathon],
Le, Q.V.[Quoc V.],
Learning Data Augmentation Strategies for Object Detection,
ECCV20(XXVII:566-583).
Springer DOI
2011
BibRef
Novozámský, A.,
Vít, D.,
Šroubek, F.,
Franc, J.,
Krbálek, M.,
Bílkova, Z.,
Zitová, B.,
Automated Object Labeling for CNN-Based Image Segmentation,
ICIP20(2036-2040)
IEEE DOI
2011
Generating training data for CNN training.
Image segmentation, Training, Labeling, Automobiles, Task analysis,
Coolants, Training data, CNN, SURF, U-net, automated object labeling,
image segmentation
BibRef
Zhang, K.,
Cao, Z.,
Wu, J.,
Circular Shift: An Effective Data Augmentation Method For
Convolutional Neural Network On Image Classification,
ICIP20(1676-1680)
IEEE DOI
2011
Training, Agriculture, Task analysis, Data visualization,
Image resolution, Neural networks, Data models, circular shift,
convolutional neural network
BibRef
Dionelis, N.[Nikolaos],
Yaghoobi, M.[Mehrdad],
Tsaftaris, S.A.[Sotirios A.],
Boundary Of Distribution Support Generator (BDSG):
Sample Generation On The Boundary,
ICIP20(803-807)
IEEE DOI
2011
Anomaly detection, Generators, Data models,
Convergence, Estimation, Computational modeling,
Anomaly detection, invertible models
BibRef
Tong, Y.H.[Yun-He],
Wang, A.[Anjie],
Tan, S.C.[Song-Chao],
Wang, S.S.[Shan-She],
Ma, S.W.[Si-Wei],
Gao, W.[Wen],
Self-Supervised Learning of Depth and Pose Using Cycle Generative
Adversarial Network,
ICIP20(738-742)
IEEE DOI
2011
Indexes, Self-supervised learning, CycleGAN, depth estimation,
pose estimation, monocular
BibRef
Tang, Z.Q.[Zhi-Qiang],
Gao, Y.H.[Yun-He],
Karlinsky, L.[Leonid],
Sattigeri, P.[Prasanna],
Feris, R.S.[Rogerio S.],
Metaxas, D.N.[Dimitris N.],
Onlineaugment: Online Data Augmentation with Less Domain Knowledge,
ECCV20(VII:313-329).
Springer DOI
2011
BibRef
Ryoo, M.S.[Michael S.],
Piergiovanni, A.J.,
Kangaspunta, J.[Juhana],
Angelova, A.[Anelia],
Assemblenet++: Assembling Modality Representations via Attention
Connections,
ECCV20(XX:654-671).
Springer DOI
2011
BibRef
Wang, X.F.[Xiao-Fang],
Xiong, X.[Xuehan],
Neumann, M.[Maxim],
Piergiovanni, A.J.,
Ryoo, M.S.[Michael S.],
Angelova, A.[Anelia],
Kitani, K.M.[Kris M.],
Hua, W.[Wei],
Attentionnas:
Spatiotemporal Attention Cell Search for Video Classification,
ECCV20(VIII:449-465).
Springer DOI
2011
BibRef
Behl, H.S.[Harkirat Singh],
Baydin, A.G.[Atilim Günes],
Gal, R.[Ran],
Torr, P.H.S.[Philip H. S.],
Vineet, V.[Vibhav],
Autosimulate: (quickly) Learning Synthetic Data Generation,
ECCV20(XXII:255-271).
Springer DOI
2011
BibRef
Biffi, C.[Carlo],
McDonagh, S.[Steven],
Torr, P.H.S.[Philip H.S.],
Leonardis, A.[Aleš],
Parisot, S.[Sarah],
Many-shot from Low-shot:
Learning to Annotate Using Mixed Supervision for Object Detection,
ECCV20(VIII:35-50).
Springer DOI
2011
Generate large number of annotations from a larger set of weakly labelled
images.
BibRef
Shetty, R.[Rakshith],
Fritz, M.[Mario],
Schiele, B.[Bernt],
Towards Automated Testing and Robustification by Semantic Adversarial
Data Generation,
ECCV20(II:489-506).
Springer DOI
2011
BibRef
Olut, S.[Sahin],
Shen, Z.Y.[Zheng-Yang],
Xu, Z.L.[Zhen-Lin],
Gerber, S.[Samuel],
Niethammer, M.[Marc],
Adversarial Data Augmentation via Deformation Statistics,
ECCV20(XXIX: 643-659).
Springer DOI
2010
BibRef
Chu, P.[Peng],
Bian, X.[Xiao],
Liu, S.P.[Shao-Peng],
Ling, H.B.[Hai-Bin],
Feature Space Augmentation for Long-tailed Data,
ECCV20(XXIX: 694-710).
Springer DOI
2010
BibRef
Bernal, E.A.[Edgar A.],
Training Deep Generative Models in Highly Incomplete Data Scenarios
with Prior Regularization,
LLID21(2631-2641)
IEEE DOI
2109
Training, Computational modeling,
Statistical distributions, Data models, Filling
BibRef
Richardson, T.W.[Trevor W.],
Wu, W.C.[Wen-Cheng],
Lin, L.[Lei],
Xu, B.L.[Bei-Lei],
Bernal, E.A.[Edgar A.],
McFlow: Monte Carlo Flow Models for Data Imputation,
CVPR20(14193-14202)
IEEE DOI
2008
Issues of missing data.
Data models, Task analysis, Monte Carlo methods,
Computational modeling, Training, Training data
BibRef
Koutilya, P.N.V.R.,
Zhou, H.[Hao],
Jacobs, D.[David],
SharinGAN: Combining Synthetic and Real Data for Unsupervised
Geometry Estimation,
CVPR20(13971-13980)
IEEE DOI
2008
combining synthetic and real images when training.
Task analysis, Estimation, Geometry, Training, Semantics,
Image reconstruction
BibRef
Hoffer, E.,
Ben-Nun, T.,
Hubara, I.,
Giladi, N.,
Hoefler, T.,
Soudry, D.,
Augment Your Batch: Improving Generalization Through Instance
Repetition,
CVPR20(8126-8135)
IEEE DOI
2008
Training, Neural networks, Convergence, Correlation, Schedules,
Standards, Task analysis
BibRef
Jaipuria, N.,
Zhang, X.,
Bhasin, R.,
Arafa, M.,
Chakravarty, P.,
Shrivastava, S.,
Manglani, S.,
Murali, V.N.,
Deflating Dataset Bias Using Synthetic Data Augmentation,
DeepVision20(3344-3353)
IEEE DOI
2008
Task analysis, Data models, Training, Estimation,
Autonomous vehicles
BibRef
Kushwaha, A.,
Gupta, S.,
Bhanushali, A.,
Dastidar, T.R.,
Rapid Training Data Creation by Synthesizing Medical Images for
Classification and Localization,
Microscopy20(4272-4279)
IEEE DOI
2008
Training, Biomedical imaging, Solid modeling, Data models,
Microscopy, Object detection, Machine learning
BibRef
Cubuk, E.D.,
Zoph, B.,
Shlens, J.,
Le, Q.V.,
Randaugment: Practical automated data augmentation with a reduced
search space,
EDLCV20(3008-3017)
IEEE DOI
2008
Task analysis, Distortion, Data models, Training, Noise measurement,
Computational modeling
BibRef
Luo, C.,
Zhu, Y.,
Jin, L.,
Wang, Y.,
Learn to Augment: Joint Data Augmentation and Network Optimization
for Text Recognition,
CVPR20(13743-13752)
IEEE DOI
2008
Text recognition, Training, Writing, Shape, Robustness, Optimization,
Task analysis
BibRef
Smith, P.,
Ricanek, K.,
Mitigating Algorithmic Bias:
Evolving an Augmentation Policy that is Non-Biasing,
WACVWS20(90-97)
IEEE DOI
2006
Face, Data models, Training, Neural networks, Encyclopedias,
Electronic publishing
BibRef
Mordido, G.,
Yang, H.,
Meinel, C.,
microbatchGAN: Stimulating Diversity with Multi-Adversarial
Discrimination,
WACV20(3050-3059)
IEEE DOI
2006
Training, Task analysis, Generators, Games,
Data models, Focusing
BibRef
Kar, A.,
Prakash, A.,
Liu, M.,
Cameracci, E.,
Yuan, J.,
Rusiniak, M.,
Acuna, D.,
Torralba, A.,
Fidler, S.,
Meta-Sim: Learning to Generate Synthetic Datasets,
ICCV19(4550-4559)
IEEE DOI
2004
grammars, graph theory, image processing,
learning (artificial intelligence), neural nets, probability, Engines
BibRef
Bello, I.,
Zoph, B.,
Le, Q.,
Vaswani, A.,
Shlens, J.,
Attention Augmented Convolutional Networks,
ICCV19(3285-3294)
IEEE DOI
2004
convolutional neural nets, image classification,
learning (artificial intelligence), object detection, Encoding
BibRef
Tang, Z.Q.[Zhi-Qiang],
Peng, X.[Xi],
Li, T.F.[Ting-Feng],
Zhu, Y.Z.[Yi-Zhe],
Metaxas, D.N.[Dimitris N.],
AdaTransform: Adaptive Data Transformation,
ICCV19(2998-3006)
IEEE DOI
2004
learning (artificial intelligence), neural nets,
high computational cost, leverage data transformation, Transforms
BibRef
Noguchi, A.,
Harada, T.,
Image Generation From Small Datasets via Batch Statistics Adaptation,
ICCV19(2750-2758)
IEEE DOI
2004
image processing, neural nets, statistical analysis,
image generation, small datasets, batch statistics adaptation, Convolution
BibRef
Chen, W.,
Tian, L.,
Fan, L.,
Wang, Y.,
Augmentation Invariant Training,
CEFRL19(2963-2971)
IEEE DOI
2004
learning (artificial intelligence), neural net architecture,
neural nets, insufficient generalization ability,
Neural Network
BibRef
Lin, C.,
Guo, M.,
Li, C.,
Yuan, X.,
Wu, W.,
Yan, J.,
Lin, D.,
Ouyang, W.,
Online Hyper-Parameter Learning for Auto-Augmentation Strategy,
ICCV19(6578-6587)
IEEE DOI
2004
Gaussian processes,
learning (artificial intelligence), optimisation, probability, Data models
BibRef
Hinterstoisser, S.[Stefan],
Pauly, O.[Olivier],
Heibel, H.[Hauke],
Martina, M.[Marek],
Bokeloh, M.[Martin],
An Annotation Saved is an Annotation Earned:
Using Fully Synthetic Training for Object Detection,
R6D19(2787-2796)
IEEE DOI
2004
Create synthetic data for training.
image texture, learning (artificial intelligence), neural nets,
object detection, realistic images, Deep Learning
BibRef
Fong, R.,
Occlusions for Effective Data Augmentation in Image Classification,
VXAI19(4158-4166)
IEEE DOI
2004
image classification, neural nets, occlusions,
data augmentation, image classification, deep networks,
deep-learning
BibRef
Carlucci, F.M.,
Russo, P.,
Tommasi, T.,
Caputo, B.,
Hallucinating Agnostic Images to Generalize Across Domains,
TASKCV19(3227-3234)
IEEE DOI
2004
image classification, learning (artificial intelligence),
adversarial domain classifier, unlabeled target samples,
multisource domain adaptation
BibRef
Hong, S.E.[Sung-Eun],
Kang, S.I.[Sung-Il],
Cho, D.H.[Dong-Hyeon],
Patch-Level Augmentation for Object Detection in Aerial Images,
VisDrone19(127-134)
IEEE DOI
2004
data mining, feature extraction, image classification,
image representation, image segmentation, class imbalance
BibRef
Yu, A.[Aron],
Grauman, K.[Kristen],
Thinking Outside the Pool: Active Training Image Creation for Relative
Attributes,
CVPR19(708-718).
IEEE DOI
2002
BibRef
Cubuk, E.D.[Ekin D.],
Zoph, B.[Barret],
Mane, D.[Dandelion],
Vasudevan, V.[Vijay],
Le, Q.V.[Quoc V.],
AutoAugment: Learning Augmentation Strategies From Data,
CVPR19(113-123).
IEEE DOI
2002
BibRef
Luo, Z.X.[Zi-Xin],
Shen, T.W.[Tian-Wei],
Zhou, L.[Lei],
Zhang, J.R.[Jia-Rui],
Yao, Y.[Yao],
Li, S.W.[Shi-Wei],
Fang, T.[Tian],
Quan, L.[Long],
ContextDesc: Local Descriptor Augmentation With Cross-Modality Context,
CVPR19(2522-2531).
IEEE DOI
2002
BibRef
Alipourfard, T.,
Arefi, H.,
Virtual Training Sample Generation by Generative Adversarial Networks
for Hyperspectral Images Classification,
SMPR19(63-69).
DOI Link
1912
BibRef
Hoffmann, D.T.[David T.],
Tzionas, D.[Dimitrios],
Black, M.J.[Michael J.],
Tang, S.[Siyu],
Learning to Train with Synthetic Humans,
GCPR19(609-623).
Springer DOI
1911
BibRef
Chen, Z.,
Huang, Y.,
Wang, L.,
Augmented Visual-Semantic Embeddings for Image and Sentence Matching,
ICIP19(290-294)
IEEE DOI
1910
Generative Adversarial Networks, Image and Sentence Matching,
Visual-Semantic Embeddings
BibRef
Dou, Y.M.[Yi-Min],
Yu, X.R.[Xiang-Ru],
Li, J.P.[Jin-Ping],
Feature GANs: A Model for Data Enhancement and Sample Balance of
Foreign Object Detection in High Voltage Transmission Lines,
CAIP19(II:568-580).
Springer DOI
1909
BibRef
Bongini, P.[Pietro],
del Chiaro, R.[Riccardo],
Bagdanov, A.D.[Andrew D.],
del Bimbo, A.[Alberto],
GADA: Generative Adversarial Data Augmentation for Image Quality
Assessment,
CIAP19(II:214-224).
Springer DOI
1909
BibRef
Aguilar, E.[Eduardo],
Radeva, P.[Petia],
Class-Conditional Data Augmentation Applied to Image Classification,
CAIP19(II:182-192).
Springer DOI
1909
BibRef
Carlson, A.[Alexandra],
Skinner, K.A.[Katherine A.],
Vasudevan, R.[Ram],
Johnson-Roberson, M.[Matthew],
Modeling Camera Effects to Improve Visual Learning from Synthetic Data,
VLEASE18(I:505-520).
Springer DOI
1905
learning visual tasks in urban scenes.
BibRef
Liu, S.J.[Shuang-Jun],
Ostadabbas, S.[Sarah],
A Semi-supervised Data Augmentation Approach Using 3D Graphical Engines,
HBU18(II:395-408).
Springer DOI
1905
BibRef
Milz, S.[Stefan],
Rüdiger, T.[Tobias],
Süss, S.[Sebastian],
Aerial GANeration: Towards Realistic Data Augmentation Using
Conditional GANs,
CVUAV18(II:59-72).
Springer DOI
1905
BibRef
Patel, V.,
Mujumdar, N.,
Balasubramanian, P.,
Marvaniya, S.,
Mittal, A.,
Data Augmentation Using Part Analysis for Shape Classification,
WACV19(1223-1232)
IEEE DOI
1904
convolutional neural nets, feature extraction,
image classification, learning (artificial intelligence),
Optimization
BibRef
Summers, C.,
Dinneen, M.J.,
Improved Mixed-Example Data Augmentation,
WACV19(1262-1270)
IEEE DOI
1904
learning (artificial intelligence), neural nets,
pattern classification, additional training data,
Computer science
BibRef
Behpour, S.,
Kitani, K.,
Ziebart, B.,
ADA: Adversarial Data Augmentation for Object Detection,
WACV19(1243-1252)
IEEE DOI
1904
computational complexity, game theory,
learning (artificial intelligence), object detection, Pascal,
Object detection
BibRef
Pang, K.K.[Kun-Kun],
Dong, M.Z.[Ming-Zhi],
Wu, Y.[Yang],
Hospedales, T.M.[Timothy M.],
Dynamic Ensemble Active Learning:
A Non-Stationary Bandit with Expert Advice,
ICPR18(2269-2276)
IEEE DOI
1812
Heuristic algorithms, Uncertainty, Prediction algorithms, Switches,
Training, Tuning
BibRef
Beluch, W.H.[William H.],
Genewein, T.[Tim],
Nurnberger, A.[Andreas],
Kohler, J.M.[Jan M.],
The Power of Ensembles for Active Learning in Image Classification,
CVPR18(9368-9377)
IEEE DOI
1812
Uncertainty, Neural networks, Labeling, Training,
Monte Carlo methods, Data models
BibRef
Gasparetto, A.,
Ressi, D.,
Bergamasco, F.,
Pistellato, M.,
Cosmo, L.,
Boschetti, M.,
Ursella, E.,
Albarelli, A.,
Cross-Dataset Data Augmentation for Convolutional Neural Networks
Training,
ICPR18(910-915)
IEEE DOI
1812
feedforward neural nets, learning (artificial intelligence),
neural nets, convolutional neural networks training,
Transforms
BibRef
Shi, H.J.[Hong-Jiang],
Wang, L.[Lu],
Ding, G.T.[Guang-Tai],
Yang, F.L.[Feng-Lei],
Li, X.Q.[Xiao-Qiang],
Data Augmentation with Improved Generative Adversarial Networks,
ICPR18(73-78)
IEEE DOI
1812
Generative adversarial networks, Training,
Generators, Neural networks, Task analysis, Stochastic processes
BibRef
Liu, X.,
Zou, Y.,
Kong, L.,
Diao, Z.,
Yan, J.,
Wang, J.,
Li, S.,
Jia, P.,
You, J.,
Data Augmentation via Latent Space Interpolation for Image
Classification,
ICPR18(728-733)
IEEE DOI
1812
Interpolation, Training, Training data,
Neural networks, Generative adversarial networks,
inter-class sampling
BibRef
Huang, S.W.[Sheng-Wei],
Lin, C.T.[Che-Tsung],
Chen, S.P.[Shu-Ping],
Wu, Y.Y.[Yen-Yi],
Hsu, P.H.[Po-Hao],
Lai, S.H.[Shang-Hong],
AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation,
ECCV18(IX: 731-744).
Springer DOI
1810
BibRef
Liu, B.[Bo],
Wang, X.D.[Xu-Dong],
Dixit, M.[Mandar],
Kwitt, R.[Roland],
Vasconcelos, N.M.[Nuno M.],
Feature Space Transfer for Data Augmentation,
CVPR18(9090-9098)
IEEE DOI
1812
Trajectory, Manifolds, Feature extraction, Task analysis, Shape, Decoding
BibRef
Merchant, A.,
Syed, T.,
Khan, B.,
Munir, R.,
Appearance-based data augmentation for image datasets using contrast
preserving sampling,
ICPR18(1235-1240)
IEEE DOI
1812
Kernel, Convolutional neural networks, Shape, Tensile stress,
Error analysis, Agriculture, Data models,
constraint graph
BibRef
Elezi, I.,
Torcinovich, A.,
Vascon, S.,
Pelillo, M.,
Transductive Label Augmentation for Improved Deep Network Learning,
ICPR18(1432-1437)
IEEE DOI
1812
Games, Labeling, Standards, Neural networks,
Feature extraction, Training
BibRef
Nguyen, T.D.,
Nguyen, V.,
Le, T.,
Phung, D.,
Distributed data augmented support vector machine on Spark,
ICPR16(498-503)
IEEE DOI
1705
Data models, Distributed databases, Estimation, Industries,
Scalability, Sparks, Support vector machines, Apache Spark, big data,
distributed computing, large-scale classification, support, vector, machine
BibRef
d'Innocente, A.[Antonio],
Carlucci, F.M.[Fabio Maria],
Colosi, M.[Mirco],
Caputo, B.[Barbara],
Bridging Between Computer and Robot Vision Through Data Augmentation:
A Case Study on Object Recognition,
CVS17(384-393).
Springer DOI
1711
BibRef
Wong, S.C.,
Gatt, A.,
Stamatescu, V.,
McDonnell, M.D.,
Understanding Data Augmentation for Classification: When to Warp?,
DICTA16(1-6)
IEEE DOI
1701
BibRef
Fawzi, A.[Alhussein],
Samulowitz, H.[Horst],
Turaga, D.[Deepak],
Frossard, P.[Pascal],
Adaptive data augmentation for image classification,
ICIP16(3688-3692)
IEEE DOI
1610
Approximation algorithms. Adding more samples.
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Metric Learning .