14.5.2.2 Meta-Learning

Chapter Contents (Back)
Learning. Meta-Learning.

Zhang, P.[Pei], Bai, Y.P.[Yun-Peng], Wang, D.[Dong], Bai, B.[Bendu], Li, Y.[Ying],
Few-Shot Classification of Aerial Scene Images via Meta-Learning,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Deng, F.Q.[Fu-Qin], Zhong, J.M.[Jia-Ming], Li, N.N.[Nan-Nan], Fu, L.H.[Lan-Hui], Wang, D.[Dong], Lam, T.L.[Tin Lun],
Exploring Cross-Video Matching for Few-Shot Video Classification via Dual-Hierarchy Graph Neural Network Learning,
IVC(139), 2023, pp. 104822.
Elsevier DOI Code:
WWW Link. 2311
Video classification, Few-shot learning, Hierarchy graph neural network BibRef

Xu, H.[Hui], Wang, J.X.[Jia-Xing], Li, H.[Hao], Ouyang, D.Q.[De-Qiang], Shao, J.[Jie],
Unsupervised meta-learning for few-shot learning,
PR(116), 2021, pp. 107951.
Elsevier DOI 2106
Unsupervised learning, Meta-learning, Few-shot learning BibRef

Zhang, B.Q.[Bao-Quan], Leung, K.C.[Ka-Cheong], Li, X.T.[Xu-Tao], Ye, Y.M.[Yun-Ming],
Learn to abstract via concept graph for weakly-supervised few-shot learning,
PR(117), 2021, pp. 107946.
Elsevier DOI 2106
Few-shot learning, Weakly-supervised learning, Meta-learning, Concept graph BibRef

Li, Y.[Yong], Shao, Z.F.[Zhen-Feng], Huang, X.[Xiao], Cai, B.[Bowen], Peng, S.[Song],
Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Li, H.F.[Hai-Feng], Cui, Z.Q.[Zhen-Qi], Zhu, Z.Q.[Zhi-Qiang], Chen, L.[Li], Zhu, J.W.[Jia-Wei], Huang, H.Z.[Hao-Zhe], Tao, C.[Chao],
RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification,
GeoRS(59), No. 8, August 2021, pp. 6983-6994.
IEEE DOI 2108
Task analysis, Remote sensing, Measurement, Training, Neural networks, Feature extraction, Data models, remote sensing classification BibRef

Doveh, S.[Sivan], Schwartz, E.[Eli], Xue, C.[Chao], Feris, R.S.[Rogerio S.], Bronstein, A.M.[Alex M.], Giryes, R.[Raja], Karlinsky, L.[Leonid],
MetAdapt: Meta-learned task-adaptive architecture for few-shot classification,
PRL(149), 2021, pp. 130-136.
Elsevier DOI 2108
BibRef

Chen, X.Y.[Xiang-Yu], Wang, G.H.[Guang-Hui],
Few-Shot Learning by Integrating Spatial and Frequency Representation,
CRV21(49-56)
IEEE DOI 2108
Machine learning algorithms, Frequency-domain analysis, Machine learning, Classification algorithms, frequency information BibRef

Singh, R.[Rishav], Bharti, V.[Vandana], Purohit, V.[Vishal], Kumar, A.[Abhinav], Singh, A.K.[Amit Kumar], Singh, S.K.[Sanjay Kumar],
MetaMed: Few-shot medical image classification using gradient-based meta-learning,
PR(120), 2021, pp. 108111.
Elsevier DOI 2109
Few-shot learning, Meta-learning, Multi-shot learning, Medical image classification, Image augmentation, Histopathological image classification BibRef

Li, X.Z.[Xin-Zhe], Huang, J.Q.[Jian-Qiang], Liu, Y.Y.[Yao-Yao], Zhou, Q.[Qin], Zheng, S.[Shibao], Schiele, B.[Bernt], Sun, Q.R.[Qian-Ru],
Learning to teach and learn for semi-supervised few-shot image classification,
CVIU(212), 2021, pp. 103270.
Elsevier DOI 2110
Few-shot learning, Meta-learning, Semi-supervised learning BibRef

Zimmer, L.[Lucas], Lindauer, M.[Marius], Hutter, F.[Frank],
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL,
PAMI(43), No. 9, September 2021, pp. 3079-3090.
IEEE DOI 2108
Optimization, Open area test sites, Training, Benchmark testing, Task analysis, Pipelines, meta-learning BibRef

Hu, Z.P.[Zheng-Ping], Li, Z.J.[Zi-Jun], Wang, X.Y.[Xue-Yu], Zheng, S.[Saiyue],
Unsupervised descriptor selection based meta-learning networks for few-shot classification,
PR(122), 2022, pp. 108304.
Elsevier DOI 2112
Meta-learning, Few-shot classification, Unsupervised localization, Descriptor selection BibRef

Cui, Y.W.[Ya-Wen], Liao, Q.[Qing], Hu, D.[Dewen], An, W.[Wei], Liu, L.[Li],
Coarse-to-fine pseudo supervision guided meta-task optimization for few-shot object classification,
PR(122), 2022, pp. 108296.
Elsevier DOI 2112
Unsupervised few-shot learning, Meta-learning, Clustering, Object classification BibRef

Ji, Z.[Zhong], Hou, Z.S.[Zhi-Shen], Liu, X.[Xiyao], Pang, Y.W.[Yan-Wei], Han, J.G.[Jun-Gong],
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning,
IP(31), 2022, pp. 1520-1531.
IEEE DOI 2202
Semantics, Visualization, Task analysis, Training, Correlation, Sun, Learning systems, Few-shot learning, meta-learning, multi-modal, graph propagation BibRef

Li, F.[Feimo], Li, S.B.[Shuai-Bo], Fan, X.X.[Xin-Xin], Li, X.[Xiong], Chang, H.X.[Hong-Xing],
Structural Attention Enhanced Continual Meta-Learning for Graph Edge Labeling Based Few-Shot Remote Sensing Scene Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhou, F.[Fei], Zhang, L.[Lei], Wei, W.[Wei],
Meta-Generating Deep Attentive Metric for Few-Shot Classification,
CirSysVideo(32), No. 10, October 2022, pp. 6863-6873.
IEEE DOI 2210
Measurement, Task analysis, Training, Gaussian distribution, Optimization, Standards, Feature extraction, Few-shot learning, meta-learning BibRef

Guo, T.[Ting], Liang, J.Q.[Jian-Qing], Liang, J.[Jiye], Xie, G.S.[Guo-Sen],
Cross-modal propagation network for generalized zero-shot learning,
PRL(159), 2022, pp. 125-131.
Elsevier DOI 2206
Zero-shot learning, Generative adversarial network, Meta-learning, Label propagation BibRef

Zhang, J.[Ji], Song, J.K.[Jing-Kuan], Gao, L.[Lianli], Liu, Y.[Ye], Shen, H.T.[Heng Tao],
Progressive Meta-Learning With Curriculum,
CirSysVideo(32), No. 9, September 2022, pp. 5916-5930.
IEEE DOI 2209
Task analysis, Training, Adaptation models, Computational modeling, Ear, Standards, Pediatrics, Few-shot learning, meta-learning, hard task-sampling BibRef

Hu, Z.[Ziye], Li, W.[Wei], Gan, Z.X.[Zhong-Xue], Guo, W.[Weikun], Zhu, J.[Jiwei], Wen, J.Z.Q.[James Zhi-Qing], Zhou, D.[Decheng],
Learning From Visual Demonstrations via Replayed Task-Contrastive Model-Agnostic Meta-Learning,
CirSysVideo(32), No. 12, December 2022, pp. 8756-8767.
IEEE DOI 2212
Robots, Microstrip, Visualization, Adaptation models, Training data, Reinforcement learning, Meta-learning, learning to learn BibRef

Bing, Z.S.[Zhen-Shan], Lerch, D.[David], Huang, K.[Kai], Knoll, A.[Alois],
Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments,
PAMI(45), No. 3, March 2023, pp. 3476-3491.
IEEE DOI 2302
Task analysis, Training, Robots, Adaptation models, Multitasking, Inference algorithms, Gaussian mixture model, robotic control BibRef

Martins, V.E.[Vinicius Eiji], Cano, A.[Alberto], Barbon Junior, S.[Sylvio],
Meta-learning for dynamic tuning of active learning on stream classification,
PR(138), 2023, pp. 109359.
Elsevier DOI 2303
Meta-learning, Active learning, Data stream, Concept drift BibRef

Li, Y.[Yun], Liu, Z.[Zhe], Yao, L.[Lina], Chang, X.J.[Xiao-Jun],
Attribute-Modulated Generative Meta Learning for Zero-Shot Learning,
MultMed(25), 2023, pp. 1600-1610.
IEEE DOI 2306
Task analysis, Modulation, Adaptation models, Visualization, Training, Generators, Semantics, Zero-shot learning, meta-learning, image retrieval BibRef

Jiang, S.Q.[Shu-Qiang], Zhu, Y.[Yaohui], Liu, C.L.[Chen-Long], Song, X.H.[Xin-Hang], Li, X.Y.[Xiang-Yang], Min, W.Q.[Wei-Qing],
Dataset Bias in Few-Shot Image Recognition,
PAMI(45), No. 1, January 2023, pp. 229-246.
IEEE DOI 2212
Task analysis, Visualization, Learning systems, Adaptation models, Image recognition, Training, Complexity theory, Dataset bias, meta-learning BibRef

Zhang, L.[Lei], Zhou, F.[Fei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning],
Meta-hallucinating prototype for few-shot learning promotion,
PR(136), 2023, pp. 109235.
Elsevier DOI 2301
Few-shot learning, Prototype hallucination, Meta-learning BibRef

Cheng, J.[Jun], Hao, F.[Fusheng], He, F.X.[Feng-Xiang], Liu, L.[Liu], Zhang, Q.[Qieshi],
Mixer-Based Semantic Spread for Few-Shot Learning,
MultMed(25), 2023, pp. 191-202.
IEEE DOI 2301
Semantics, Feature extraction, Training, Mixers, Task analysis, Visualization, Few-shot learning, metric learning-based meta-learning BibRef

Ye, H.J.[Han-Jia], Han, L.[Lu], Zhan, D.C.[De-Chuan],
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks,
PAMI(45), No. 3, March 2023, pp. 3721-3737.
IEEE DOI 2302
Task analysis, Unified modeling language, Training, Feature extraction, Semantics, Labeling, Visualization, self-supervised learning BibRef

Tabealhojeh, H.[Hadi], Adibi, P.[Peyman], Karshenas, H.[Hossein], Roy, S.K.[Soumava Kumar], Harandi, M.[Mehrtash],
RMAML: Riemannian meta-learning with orthogonality constraints,
PR(140), 2023, pp. 109563.
Elsevier DOI 2305
Meta-learning, Geometry-aware optimization, Riemannian manifolds, Few-shot image classification BibRef

Zhao, Y.Q.[Yun-Qing], Cheung, N.M.[Ngai-Man],
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification,
IP(32), 2023, pp. 2252-2266.
IEEE DOI 2305
Training, Power capacitors, Task analysis, Data models, Knowledge engineering, Adaptation models, Training data, meta-learning BibRef

Zhang, B.Q.[Bao-Quan], Jiang, H.[Hao], Li, X.[Xutao], Feng, S.S.[Shan-Shan], Ye, Y.M.[Yun-Ming], Luo, C.[Chen], Ye, R.[Rui],
MetaDT: Meta Decision Tree With Class Hierarchy for Interpretable Few-Shot Learning,
CirSysVideo(33), No. 6, June 2023, pp. 2826-2838.
IEEE DOI 2306
Decision trees, Visualization, Dogs, Task analysis, Semantics, Neural networks, Heating systems, Few-shot learning, meta-learning, class hierarchy BibRef

Peng, D.[Danni], Pan, S.J.L.[Sinno Jia-Lin],
Clustered Task-Aware Meta-Learning by Learning from Learning Paths,
PAMI(45), No. 8, August 2023, pp. 9426-9438.
IEEE DOI 2307

WWW Link. Task analysis, Training, Feature extraction, Modulation, Trajectory, Optimization, Knowledge engineering, Task clustering, task-aware meta-learning BibRef

Gao, Z.[Zhi], Wu, Y.W.[Yu-Wei], Harandi, M.[Mehrtash], Jia, Y.D.[Yun-De],
Curvature-Adaptive Meta-Learning for Fast Adaptation to Manifold Data,
PAMI(45), No. 2, February 2023, pp. 1545-1562.
IEEE DOI 2301
Manifolds, Task analysis, Optimization, Neural networks, Adaptation models, Geometry, Data models, Meta-learning, curvature BibRef

Nguyen, C.[Cuong], Do, T.T.[Thanh-Toan], Carneiro, G.[Gustavo],
PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors,
PAMI(45), No. 1, January 2023, pp. 841-851.
IEEE DOI 2212
Task analysis, Data models, Training, Adaptation models, Optimization, Predictive models, Gaussian distribution, PAC bayes, transfer learning BibRef

Baik, S.[Sungyong], Choi, M.[Myungsub], Choi, J.[Janghoon], Kim, H.[Heewon], Lee, K.M.[Kyoung Mu],
Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning,
PAMI(46), No. 3, March 2024, pp. 1441-1454.
IEEE DOI 2402
Task analysis, Optimization, Mathematical models, Adaptation models, Visualization, Training, Neural networks, visual tracking BibRef

Baik, S.[Sungyong], Choi, J.[Janghoon], Kim, H.[Heewon], Cho, D.[Dohee], Min, J.[Jaesik], Lee, K.M.[Kyoung Mu],
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning,
ICCV21(9445-9454)
IEEE DOI 2203
Learning systems, Metals, Task analysis, Optimization, Transfer/Low-shot/Semi/Unsupervised Learning, Efficient training and inference methods BibRef

Wang, R.[Ruohan], Falk, J.I.T.[John Isak Texas], Pontil, M.[Massimiliano], Ciliberto, C.[Carlo],
Robust Meta-Representation Learning via Global Label Inference and Classification,
PAMI(46), No. 4, April 2024, pp. 1996-2010.
IEEE DOI 2403
Task analysis, Metalearning, Training, Standards, Feature extraction, Adaptation models, Merging, Few-Shot image classification, representation learning BibRef

Wang, C.[Chengkun], Zheng, W.Z.[Wen-Zhao], Zhu, Z.[Zheng], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Introspective Deep Metric Learning,
PAMI(46), No. 4, April 2024, pp. 1964-1980.
IEEE DOI 2403
Measurement, Uncertainty, Semantics, Training, Measurement uncertainty, Probabilistic logic, Image retrieval, uncertainty-aware similarity judgments BibRef

Yan, X.G.[Xin-Gao], Shao, F.[Feng], Chen, H.W.[Hang-Wei], Jiang, Q.P.[Qiu-Ping],
Hybrid CNN-transformer based meta-learning approach for personalized image aesthetics assessment,
JVCIR(98), 2024, pp. 104044.
Elsevier DOI 2402
Meta-Learning, Personalized image aesthetics assessment BibRef

Zhang, W.[Wei], Wang, X.S.[Xue-Song], Wang, H.Y.[Hao-Yu], Cheng, Y.[Yuhu],
Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification,
RS(16), No. 6, 2024, pp. 1055.
DOI Link 2403
BibRef

Wang, Y.[Yi], Huang, C.Q.[Chang-Qin], Li, M.[Ming], Huang, Q.[Qionghao], Wu, X.M.[Xue-Mei], Wu, J.[Jia],
AG-Meta: Adaptive graph meta-learning via representation consistency over local subgraphs,
PR(151), 2024, pp. 110387.
Elsevier DOI 2404
Embedding representation, Local subgraphs, Few-shot graph learning, Meta-learning BibRef


Cetin, E.[Edoardo], Carta, A.[Antonio], Celiktutan, O.[Oya],
A Simple Recipe to Meta-Learn Forward and Backward Transfer,
ICCV23(18686-18696)
IEEE DOI 2401
BibRef

Wang, L.Z.[Lian-Zhe], Zhou, S.[Shiji], Zhang, S.H.[Shang-Hang], Chu, X.[Xu], Chang, H.[Heng], Zhu, W.W.[Wen-Wu],
Improving Generalization of Meta-Learning with Inverted Regularization at Inner-Level,
CVPR23(7826-7835)
IEEE DOI 2309
BibRef

Hu, Z.X.[Zi-Xuan], Shen, L.[Li], Wang, Z.[Zhenyi], Liu, T.L.[Tong-Liang], Yuan, C.[Chun], Tao, D.C.[Da-Cheng],
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning,
CVPR23(7736-7745)
IEEE DOI 2309
BibRef

Subramanyam, R.[Rakshith], Heimann, M.[Mark], Jayram, T.S., Anirudh, R.[Rushil], Thiagarajan, J.J.[Jayaraman J.],
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification,
WACV23(2478-2486)
IEEE DOI 2302
Aggregates, Semantics, Prototypes, Modulation, Benchmark testing, Encoding, Algorithms: Machine learning architectures, visual reasoning BibRef

Qin, X.R.[Xiao-Rong], Song, X.H.[Xin-Hang], Jiang, S.Q.[Shu-Qiang],
Bi-Level Meta-Learning for Few-Shot Domain Generalization,
CVPR23(15900-15910)
IEEE DOI 2309
BibRef

Kamenou, E.[Eleni], del Rincón, J.M.[Jesús Martínez], Miller, P.[Paul], Devlin-Hill, P.[Patricia],
A Meta-learning Approach for Domain Generalisation across Visual Modalities in Vehicle Re-identification,
PBVS23(385-393)
IEEE DOI 2309
BibRef

Kang, S.[Suhyun], Hwang, D.[Duhun], Eo, M.[Moonjung], Kim, T.[Taesup], Rhee, W.[Wonjong],
Meta-Learning with a Geometry-Adaptive Preconditioner,
CVPR23(16080-16090)
IEEE DOI 2309
BibRef

Ragonesi, R.[Ruggero], Morerio, P.[Pietro], Murino, V.[Vittorio],
Learning unbiased classifiers from biased data with meta-learning,
FaDE-TCV23(1-9)
IEEE DOI 2309
BibRef

Simon, C.[Christian], Koniusz, P.[Piotr], Harandi, M.[Mehrtash],
Meta-Learning for Multi-Label Few-Shot Classification,
WACV22(346-355)
IEEE DOI 2202
Microwave integrated circuits, Protocols, Predictive models, Benchmark testing, Inference algorithms, Semi- and Un- supervised Learning Deep Learning BibRef

Pan, X.H.[Xiao-Hang], Li, F.[Fanzhang],
Class-wise Attention Reinforcement for Semi-supervised Meta-Learning,
ICPR22(4479-4485)
IEEE DOI 2212
Prototypes, Benchmark testing, Task analysis BibRef

Domoguen, J.K.L.[Jansen Keith L.], Naval, P.C.[Prospero C.],
Dynamic Model-Agnostic Meta-Learning for Incremental Few-Shot Learning,
ICPR22(4927-4933)
IEEE DOI 2212
Deep learning, Adaptation models, Particle separators, Prototypes, Benchmark testing, Task analysis BibRef

Volpi, R.[Riccardo], Larlus, D.[Diane], Rogez, G.[Grégory],
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning,
CVPR21(4441-4451)
IEEE DOI 2111
Visualization, Adaptation models, Image segmentation, Computational modeling, Semantics, Pattern recognition BibRef

Cheng, Y.C.[Yuan-Chia], Lin, C.S.[Ci-Siang], Yang, F.E.[Fu-En], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains,
ICIP21(434-438)
IEEE DOI 2201
Training, Visualization, Image recognition, Target recognition, Data models, Task analysis, few-shot learning BibRef

Chen, Y.B.[Yin-Bo], Liu, Z.A.[Zhu-Ang], Xu, H.J.[Hui-Juan], Darrell, T.J.[Trevor J.], Wang, X.L.[Xiao-Long],
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning,
ICCV21(9042-9051)
IEEE DOI 2203
Training, Measurement, Codes, Classification algorithms, Task analysis, Standards, BibRef

Algan, G.[Görkem], Ulusoy, I.[Ilkay],
Meta Soft Label Generation for Noisy Labels,
ICPR21(7142-7148)
IEEE DOI 2105
Training, Degradation, Adaptation models, Neural networks, Clothing, Training data, deep learning, label noise, meta-learning BibRef

Kim, J.[Jin], Lee, J.Y.[Ji-Young], Park, J.[Jungin], Min, D.B.[Dong-Bo], Sohn, K.H.[Kwang-Hoon],
Self-Balanced Learning for Domain Generalization,
ICIP21(779-783)
IEEE DOI 2201
Training, Degradation, Adaptive systems, Image processing, Training data, Predictive models, Domain generalization, meta-learning BibRef

Jamal, M.A.[Muhammad Abdullah], Wang, L.Q.[Li-Qiang], Gong, B.Q.[Bo-Qing],
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning,
ICCV21(6557-6566)
IEEE DOI 2203
Computational modeling, Space exploration, Object recognition, Task analysis, Optimization and learning methods, Efficient training and inference methods BibRef

Shu, Y.[Yang], Cao, Z.J.[Zhang-Jie], Wang, C.Y.[Chen-Yu], Wang, J.M.[Jian-Min], Long, M.S.[Ming-Sheng],
Open Domain Generalization with Domain-Augmented Meta-Learning,
CVPR21(9619-9628)
IEEE DOI 2111
Computational modeling, Data models, Pattern recognition, Microstrip, Task analysis BibRef

Liu, B., Kang, H., Li, H., Hua, G., Vasconcelos, N.M.,
Few-Shot Open-Set Recognition Using Meta-Learning,
CVPR20(8795-8804)
IEEE DOI 2008
Training, Measurement, Task analysis, Robustness, Entropy, Image recognition, Face recognition BibRef

Rajasegaran, J., Khan, S., Hayat, M., Khan, F.S., Shah, M.,
iTAML: An Incremental Task-Agnostic Meta-learning Approach,
CVPR20(13585-13594)
IEEE DOI 2008
Task analysis, Adaptation models, Training, Stability analysis, Interference, Predictive models, Heuristic algorithms BibRef

Tseng, H.Y.[Hung-Yu], Chen, Y.W.[Yi-Wen], Tsai, Y.H.[Yi-Hsuan], Liu, S.[Sifei], Lin, Y.Y.[Yen-Yu], Yang, M.H.[Ming-Hsuan],
Regularizing Meta-learning via Gradient Dropout,
ACCV20(IV:218-234).
Springer DOI 2103
BibRef

Perrett, T.[Toby], Masullo, A.[Alessandro], Burghardt, T.[Tilo], Mirmehdi, M.[Majid], Damen, D.[Dima],
Meta-learning with Context-Agnostic Initialisations,
ACCV20(IV:70-86).
Springer DOI 2103
For few-shot by finding initial result to fine-tune. BibRef

Liu, C.H.[Cheng-Hao], Wang, Z.H.[Zhi-Hao], Sahoo, D.[Doyen], Fang, Y.[Yuan], Zhang, K.[Kun], Hoi, S.C.H.[Steven C. H.],
Adaptive Task Sampling for Meta-learning,
ECCV20(XVIII:752-769).
Springer DOI 2012
BibRef

Puri, R.[Rishi], Zakhor, A.[Avideh], Puri, R.[Raul],
Few Shot Learning For Point Cloud Data Using Model Agnostic Meta Learning,
ICIP20(1906-1910)
IEEE DOI 2011
Extend MAML. Task analysis, Feature extraction, Machine learning, Adaptation models, Neural networks, Training, 3D BibRef

Liu, Q.[Qing], Majumder, O.[Orchid], Achille, A.[Alessandro], Ravichandran, A.[Avinash], Bhotika, R.[Rahul], Soatto, S.[Stefano],
Incremental Few-shot Meta-learning via Indirect Discriminant Alignment,
ECCV20(VII:685-701).
Springer DOI 2011
BibRef

Elsken, T., Staffler, B., Metzen, J.H., Hutter, F.,
Meta-Learning of Neural Architectures for Few-Shot Learning,
CVPR20(12362-12372)
IEEE DOI 2008
Task analysis, Training, Neural networks, Adaptation models, Standards, Machine learning BibRef

Rahimpour, A., Qi, H.,
Class-Discriminative Feature Embedding For Meta-Learning based Few-Shot Classification,
WACV20(3168-3176)
IEEE DOI 2006
Task analysis, Measurement, Training, Prototypes, Predictive models, Machine learning, Data models BibRef

Li, D.[Da], Hospedales, T.M.[Timothy M.],
Online Meta-learning for Multi-source and Semi-supervised Domain Adaptation,
ECCV20(XVI: 382-403).
Springer DOI 2010
BibRef

Simon, C.[Christian], Koniusz, P.[Piotr], Nock, R.[Richard], Harandi, M.[Mehrtash],
On Modulating the Gradient for Meta-learning,
ECCV20(VIII:556-572).
Springer DOI 2011
BibRef

Jamal, M.A.[Muhammad Abdullah], Qi, G.J.[Guo-Jun],
Task Agnostic Meta-Learning for Few-Shot Learning,
CVPR19(11711-11719).
IEEE DOI 2002
BibRef

Achille, A.[Alessandro], Lam, M.[Michael], Tewari, R.[Rahul], Ravichandran, A.[Avinash], Maji, S.[Subhransu], Fowlkes, C.[Charless], Soatto, S.[Stefano], Perona, P.[Pietro],
Task2Vec: Task Embedding for Meta-Learning,
ICCV19(6429-6438)
IEEE DOI 2004
Vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks. feature extraction, image classification, learning (artificial intelligence), vectorial representations, BibRef

Ding, Y.M.[Yue-Ming], Tian, X.[Xia], Yin, L.R.[Li-Rong], Chen, X.B.[Xia-Bing], Liu, S.[Shan], Yang, B.[Bo], Zheng, W.F.[Wen-Feng],
Multi-scale Relation Network for Few-shot Learning Based on Meta-learning,
CVS19(343-352).
Springer DOI 1912
BibRef

Herath, S.[Samitha], Harandi, M.[Mehrtash], Fernando, B.[Basura], Nock, R.[Richard],
Min-Max Statistical Alignment for Transfer Learning,
CVPR19(9280-9289).
IEEE DOI 2002
BibRef

Krijthe, J.H.[Jesse H.], Ho, T.K.[Tin Kam], Loog, M.[Marco],
Improving cross-validation based classifier selection using meta-learning,
ICPR12(2873-2876).
WWW Link. 1302
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Evaluation and Analysis of Learning Techniques .


Last update:May 6, 2024 at 15:50:14