14.5.10.3 Neural Architecture, Network Structure

Banarse, D.S., Duller, A.W.G.,
Deformation Invariant Visual Object Recognition: Experiments with a Self-Organizing Neural Architecture,
NeurCompApp(6), No. 2, 1997, pp. 79-90. 9801
BibRef

Shih, F.Y.[Frank Y.], Moh, J.[Jenlong], Chang, F.C.[Fu-Chun],
A new art-based neural architecture for pattern classification and image enhancement without prior knowledge,
PR(25), No. 5, May 1992, pp. 533-542.
Elsevier DOI 0401
BibRef

Taylor, J.G., Hartley, M., Taylor, N., Panchev, C., Kasderidis, S.,
A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning,
IVC(27), No. 11, 2 October 2009, pp. 1641-1657.
Elsevier DOI 0909
Dorsal and ventral vision; Object representations; Dopamine as reward; TD learning BibRef

Lerouge, J., Herault, R., Chatelain, C., Jardin, F., Modzelewski, R.,
IODA: An input/output deep architecture for image labeling,
PR(48), No. 9, 2015, pp. 2847-2858.
Elsevier DOI 1506
Deep learning architectures BibRef

Sun, Y., Xue, B., Zhang, M., Yen, G.G., Lv, J.,
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,
Cyber(50), No. 9, September 2020, pp. 3840-3854.
IEEE DOI 2008
Tuning, Genetic algorithms, Evolutionary computation, Manuals, Genetics, Evolution (biology), neural-network architecture optimization BibRef

Zhao, J.[Junhe], Xu, S.[Sheng], Zhang, B.C.[Bao-Chang], Gu, J.X.[Jia-Xin], Doermann, D.[David], Guo, G.D.[Guo-Dong],
Towards Compact 1-bit CNNs via Bayesian Learning,
IJCV(130), No. 2, February 2022, pp. 201-225.
Springer DOI 2202
BibRef

Chen, Z.Q.[Zhi-Qiang], Xu, T.B.[Ting-Bing], Liao, W.J.[Wei-Jian], Li, Z.C.[Zheng-Cheng], Li, J.P.[Jin-Peng], Liu, C.L.[Cheng-Lin], He, H.G.[Hui-Guang],
SNAP: Shaping neural architectures progressively via information density criterion,
PR(116), 2021, pp. 107923.
Elsevier DOI 2106
Auto-generated neural architectures, Information density, Greedy strategy, Progressively, Efficient and adaptive BibRef

Khatib, R.[Rajaei], Simon, D.[Dror], Elad, M.[Michael],
Learned Greedy Method (LGM): A novel neural architecture for sparse coding and beyond,
JVCIR(77), 2021, pp. 103095.
Elsevier DOI 2106
Sparse representation, Orthogonal Matching Pursuit, Unfolding pursuit algorithms, Deraining BibRef

Tang, Y.H.[Ye-Hui], Wang, Y.H.[Yun-He], Xu, Y.X.[Yi-Xing], Chen, H.T.[Han-Ting], Shi, B.X.[Bo-Xin], Xu, C.[Chao], Xu, C.J.[Chun-Jing], Tian, Q.[Qi], Xu, C.[Chang],
A Semi-Supervised Assessor of Neural Architectures,
CVPR20(1807-1816)
IEEE DOI 2008
Training, Task analysis, Microprocessors, Optimization, Neural networks, Feature extraction BibRef

Wang, Z.Y.[Zu-Yuan],
Remote Sensing Scene Classification via Multi-Branch Local Attention Network,
IP(31), 2022, pp. 99-109.
IEEE DOI 2112
Different classes aren't that different. Remote sensing, Feature extraction, Convolutional neural networks, Image color analysis, attention mechanism BibRef

Chen, J.[Jie], Huang, H.Z.[Hao-Zhe], Peng, J.[Jian], Zhu, J.W.[Jia-Wei], Chen, L.[Li], Tao, C.[Chao], Li, H.F.[Hai-Feng],
Contextual Information-Preserved Architecture Learning for Remote-Sensing Scene Classification,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Remote sensing, Task analysis, Convolution, Architecture, Semantics, Space exploration, scene classification BibRef

Li, G.[Gen], Gu, Y.T.[Yuan-Tao], Ding, J.[Jie],
L_1 Regularization in Two-Layer Neural Networks,
SPLetters(29), 2022, pp. 135-139.
IEEE DOI 2202
Biological neural networks, Complexity theory, Neurons, Training, Approximation error, Convergence, Computational modeling, regularization BibRef

Shi, Z.L.[Zeng-Lin], Mettes, P.S.[Pascal S.], Maji, S.[Subhransu], Snoek, C.G.M.[Cees G. M.],
On Measuring and Controlling the Spectral Bias of the Deep Image Prior,
IJCV(130), No. 1, January 2022, pp. 885-908.
Springer DOI 2204
Code, Spectal Bias.
WWW Link. BibRef

Dong, N.Q.[Nan-Qing], Kampffmeyer, M.[Michael], Voiculescu, I.[Irina], Xing, E.[Eric],
Negational symmetry of quantum neural networks for binary pattern classification,
PR(129), 2022, pp. 108750.
Elsevier DOI 2206
Deep learning, Quantum machine learning, Binary pattern classification, Representation learning, Symmetry BibRef

Guo, Y.[Yong], Zheng, Y.[Yin], Tan, M.K.[Ming-Kui], Chen, Q.[Qi], Li, Z.P.[Zhi-Peng], Chen, J.[Jian], Zhao, P.L.[Pei-Lin], Huang, J.Z.[Jun-Zhou],
Towards Accurate and Compact Architectures via Neural Architecture Transformer,
PAMI(44), No. 10, October 2022, pp. 6501-6516.
IEEE DOI 2209
Optimization, Computational efficiency, Convolution, Computational modeling, Kernel, Microprocessors, operation transition BibRef

Cao, H.M.[Hui-Min], Luo, X.B.[Xiao-Bo], Peng, Y.D.[Yi-Dong], Xie, T.S.[Tian-Shou],
MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Li, H.F.[Hai-Feng], Xu, C.[Cong], Ma, L.[Lin], Bo, H.J.[Hong-Jian], Zhang, D.[David],
MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion,
PAMI(44), No. 12, December 2022, pp. 9417-9433.
IEEE DOI 2212
Neurons, Biological neural networks, Task analysis, Brain modeling, Parallel processing, Information processing, Training, parallel computing BibRef

Ao, S.[Sheng], Guo, Y.L.[Yu-Lan], Hu, Q.Y.[Qing-Yong], Yang, B.[Bo], Markham, A.[Andrew], Chen, Z.P.[Zeng-Ping],
You Only Train Once: Learning General and Distinctive 3D Local Descriptors,
PAMI(45), No. 3, March 2023, pp. 3949-3967.
IEEE DOI 2302
Feature extraction, Point cloud compression, Histograms, Shape, Transformers, Task analysis, Cross-dataset generalization, rotation invariance BibRef

Chun, I.Y.[Il Yong], Huang, Z.Y.[Zheng-Yu], Lim, H.[Hongki], Fessler, J.A.[Jeffrey A.],
Momentum-Net: Fast and Convergent Iterative Neural Network for Inverse Problems,
PAMI(45), No. 4, April 2023, pp. 4915-4931.
IEEE DOI 2303
Convergence, Artificial neural networks, Imaging, Acceleration, Image reconstruction, Optimization, Extrapolation, X-ray computational tomography BibRef

Ning, X.F.[Xue-Fei], Zheng, Y.[Yin], Zhou, Z.X.[Zi-Xuan], Zhao, T.C.[Tian-Chen], Yang, H.Z.[Hua-Zhong], Wang, Y.[Yu],
A Generic Graph-Based Neural Architecture Encoding Scheme With Multifaceted Information,
PAMI(45), No. 7, July 2023, pp. 7955-7969.
IEEE DOI 2306
BibRef
Earlier: A1, A2, A4, A6, A5, Only:
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS,
ECCV20(XIII:189-204).
Springer DOI 2011
BibRef

Rao, Y.[Yunbo], Xu, P.[Ping], Zeng, S.N.[Shao-Ning], Gou, J.P.[Jian-Ping],
Point completion by a Stack-Style Folding Network with multi-scaled graphical features,
IET-CV(17), No. 5, 2023, pp. 576-585.
DOI Link 2309
convolutional neural nets, neural net architecture BibRef

Chen, B.[Bohong], Lin, M.[Mingbao], Ji, R.R.[Rong-Rong], Cao, L.J.[Liu-Juan],
Prioritized Subnet Sampling for Resource-Adaptive Supernet Training,
PAMI(45), No. 9, September 2023, pp. 11108-11119.
IEEE DOI 2309

WWW Link. BibRef

Tian, J.[Jinkai], Sun, X.Y.[Xiao-Yu], Du, Y.X.[Yu-Xuan], Zhao, S.S.[Shan-Shan], Liu, Q.[Qing], Zhang, K.[Kaining], Yi, W.[Wei], Huang, W.[Wanrong], Wang, C.[Chaoyue], Wu, X.Y.[Xing-Yao], Hsieh, M.H.[Min-Hsiu], Liu, T.L.[Tong-Liang], Yang, W.J.[Wen-Jing], Tao, D.C.[Da-Cheng],
Recent Advances for Quantum Neural Networks in Generative Learning,
PAMI(45), No. 10, October 2023, pp. 12321-12340.
IEEE DOI 2310
BibRef

Sun, Y.H.[Yue-Heng], Jia, M.Y.[Meng-Yu], Liu, C.[Chang], Shao, M.L.[Ming-Lai],
Heterogeneous network representation learning based on role feature extraction,
PR(144), 2023, pp. 109870.
Elsevier DOI 2310
Representation learning, Role discovery, Heterogeneous network, Matrix factorization BibRef

Chen, S.F.[Shou-Fa], Xie, E.[Enze], Ge, C.J.[Chong-Jian], Chen, R.J.[Run-Jian], Liang, D.[Ding], Luo, P.[Ping],
CycleMLP: A MLP-Like Architecture for Dense Visual Predictions,
PAMI(45), No. 12, December 2023, pp. 14284-14300.
IEEE DOI 2311
multilayer perceptron. BibRef

Zhang, C.[Chao], Liwicki, S.[Stephan], He, S.[Sen], Smith, W.[William], Cipolla, R.[Roberto],
HexNet: An Orientation-Aware Deep Learning Framework for Omni-Directional Input,
PAMI(45), No. 12, December 2023, pp. 14665-14681.
IEEE DOI 2311
deep learning for spherical images. BibRef

Peng, H.Y.[Han-Yang], Yu, Y.[Yue], Yu, S.Q.[Shi-Qi],
Re-Thinking the Effectiveness of Batch Normalization and Beyond,
PAMI(46), No. 1, January 2024, pp. 465-478.
IEEE DOI 2312
BibRef

Shahadat, N.[Nazmul], Maida, A.S.[Anthony S.],
Cross channel weight sharing for image classification,
IVC(141), 2024, pp. 104872.
Elsevier DOI 2402
Hypercomplex networks, Quaternion networks, PHM layer, Axial-attention networks, Attention networks, Deep learning BibRef

Gaudet, C.J.[Chase J.], Maida, A.S.[Anthony S.],
Removing Dimensional Restrictions on Complex/Hyper-Complex Neural Networks,
ICIP21(319-323)
IEEE DOI 2201
Image color analysis, Algebra, Quaternions, Neural networks, MIMICs, Task analysis, cnn, quaternion, complex, multidimensional BibRef

Tang, Y.P.[Yi-Ping], Hatano, K.[Kohei], Takimoto, E.[Eiji],
Rotation-Invariant Convolution Networks with Hexagon-Based Kernels,
IEICE(E108-D), No. 2, February 2024, pp. 220-228.
WWW Link. 2402
BibRef

Liu, S.[Shuang], Suganuma, M.[Masanori], Okatani, T.[Takayuki],
Symmetry-aware Neural Architecture for Embodied Visual Navigation,
IJCV(132), No. 4, April 2024, pp. 1091-1107.
Springer DOI 2404
BibRef
Earlier: A1, A3, Only:
Symmetry-aware Neural Architecture for Embodied Visual Exploration,
CVPR22(17221-17230)
IEEE DOI 2210
Training, Visualization, Simultaneous localization and mapping, Convolution, Neural networks, Training data, Robot vision BibRef

Marcondes, D.[Diego], Barrera, J.[Junior],
Discrete Morphological Neural Networks,
SIIMS(17), No. 3, 2024, pp. 1650-1689.
DOI Link 2501
BibRef

Cantini, C.[Clotilde], Rolland-Piègue, E.[Emilie], Schmitter, D.[Daniel],
Exact Implementation of Closed-Form Liquid Neural Networks With Arbitrary Precision,
SPLetters(32), 2025, pp. 921-925.
IEEE DOI 2503
emulate the dynamics of biological neurons and synapses. Neurons, Synapses, Biological neural networks, Mathematical models, Electrocardiography, Training, Liquids, Data models, sampling BibRef

He, W.[Wei], Jiang, J.Y.[Jin-Yang], Liu, T.Y.[Tian-Yue], Aikoun, Y.F.[Yves Fidele], Mao, Y.[Yi],
Edge-Preserving Consensus by Exploring the Feedback-Based Masking Method,
SPLetters(32), 2025, pp. 936-940.
IEEE DOI 2503
Privacy, Protocols, Transfer functions, Eigenvalues and eigenfunctions, Signal processing algorithms, channel filter BibRef

Ma, X.[Xu], Dai, X.[Xiyang], Bai, Y.[Yue], Wang, Y.Z.[Yi-Zhou], Fu, Y.[Yun],
Rewrite the Stars,
CVPR24(5694-5703)
IEEE DOI Code:
WWW Link. 2410
Visualization, Stars, Prototypes, Solids, Polynomials, StarNet, element-wise multiplication, rewrite the stars BibRef

Chen, G.[Guikun], Li, X.[Xia], Yang, Y.[Yi], Wang, W.G.[Wen-Guan],
Neural Clustering Based Visual Representation Learning,
CVPR24(5714-5725)
IEEE DOI 2410
Representation learning, Visualization, Data analysis, Machine vision, Computational modeling, Feature extraction BibRef

Abdelouahed, S.M.[Sabri My], Abla, R.[Rahmouni], Asmae, E.[Ennaji], Abdellah, A.[Aarab],
Harnessing feature engineering to improve machine learning: A review of different data processing techniques,
ISCV24(1-6)
IEEE DOI 2408
Support vector machines, Measurement, Machine learning algorithms, Data preprocessing, Data Scaling BibRef

Abdelouahed, S.M.[Sabri My], Mohamed, E.[El_Hafci], Abdellah, A.[Aarab],
Implementing Analog Artificial Neural Networks for Enhanced Energy Efficiency and Speed in Machine Learning Applications,
ISCV24(1-6)
IEEE DOI 2408
Operational amplifiers, Accuracy, Scalability, Artificial neural networks, Machine learning, Voltage, Intensity BibRef

Carmichael, Z.[Zachariah], Redgrave, T.[Timothy], Cedre, D.G.[Daniel Gonzalez], Scheirer, W.J.[Walter J.],
This Probably Looks Exactly Like That: An Invertible Prototypical Network,
ECCV24(XXXVII: 221-240).
Springer DOI 2412
BibRef

Carmichael, Z.[Zachariah], Lohit, S.[Suhas], Cherian, A.[Anoop], Jones, M.J.[Michael J.], Scheirer, W.J.[Walter J.],
Pixel-Grounded Prototypical Part Networks,
WACV24(4756-4767)
IEEE DOI 2404
Location awareness, Training, Visualization, Neural networks, Prototypes, Space mapping, Machine learning, Algorithms, Explainable BibRef

Haque, M.[Mirazul], Yang, W.[Wei],
Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks,
REDLCV23(1489-1498)
IEEE DOI 2401
BibRef

Babiloni, F.[Francesca], Tanay, T.[Thomas], Deng, J.K.[Jian-Kang], Maggioni, M.[Matteo], Zafeiriou, S.[Stefanos],
Factorized Dynamic Fully-Connected Layers for Neural Networks,
REDLCV23(1366-1375)
IEEE DOI 2401
BibRef

Ferianc, M.[Martin], Rodrigues, M.[Miguel],
MIMMO: Multi-Input Massive Multi-Output Neural Network,
ECV23(4564-4569)
IEEE DOI 2309
BibRef

Yi, Y.[Yun], Zhang, H.[Haokui], Hu, W.Z.[Wen-Ze], Wang, N.N.[Nan-Nan], Wang, X.Y.[Xiao-Yu],
NAR-Former: Neural Architecture Representation Learning Towards Holistic Attributes Prediction,
CVPR23(7715-7724)
IEEE DOI 2309
BibRef

Shen, X.[Xuan], Wang, Y.[Yaohua], Lin, M.[Ming], Huang, Y.L.[Yi-Lun], Tang, H.[Hao], Sun, X.[Xiuyu], Wang, Y.Z.[Yan-Zhi],
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network,
CVPR23(6163-6173)
IEEE DOI 2309
BibRef

Liu, J.M.[Jin-Ming], Sun, H.M.[He-Ming], Katto, J.[Jiro],
Learned Image Compression with Mixed Transformer-CNN Architectures,
CVPR23(14388-14397)
IEEE DOI 2309
BibRef

Saxena, D.[Divya], Cao, J.[Jiannong], Xu, J.H.[Jia-Hao], Kulshrestha, T.[Tarun],
Re-GAN: Data-Efficient GANs Training via Architectural Reconfiguration,
CVPR23(16230-16240)
IEEE DOI 2309
BibRef

Guo, Y.[Yong], Chen, Y.F.[Yao-Fo], Zheng, Y.[Yin], Chen, Q.[Qi], Zhao, P.L.[Pei-Lin], Huang, J.Z.[Jun-Zhou], Chen, J.[Jian], Tan, M.K.[Ming-Kui],
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets,
NAS23(2248-2258)
IEEE DOI 2309
BibRef

Hryniowski, A.[Andrew], Wong, A.[Alexander],
Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis,
NAS23(2284-2292)
IEEE DOI 2309
BibRef

Lacharme, G.[Guillaume], Cardot, H.[Hubert], Lenté, C.[Christophe], Monmarché, N.[Nicolas],
DARTS with Degeneracy Correction,
IbPRIA23(40-53).
Springer DOI 2307
BibRef

Prach, B.[Bernd], Lampert, C.H.[Christoph H.],
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks,
ECCV22(XXI:350-365).
Springer DOI 2211

WWW Link. BibRef

Zhao, Y.[Yue], Chen, J.Z.[Jun-Zhou], Zhang, Z.[Zirui], Zhang, R.H.[Rong-Hui],
BA-Net: Bridge Attention for Deep Convolutional Neural Networks,
ECCV22(XXI:297-312).
Springer DOI 2211

WWW Link. BibRef

Davis, J.[Jim], Frank, L.[Logan],
Revisiting Batch Norm Initialization,
ECCV22(XXI:212-228).
Springer DOI 2211

WWW Link. BibRef

Trimmel, M.[Martin], Zanfir, M.[Mihai], Hartley, R.I.[Richard I.], Sminchisescu, C.[Cristian],
ERA: Enhanced Rational Activations,
ECCV22(XX:722-738).
Springer DOI 2211
ReLU BibRef

Zhou, Z.X.[Zi-Xuan], Ning, X.F.[Xue-Fei], Cai, Y.[Yi], Han, J.[Jiashu], Deng, Y.P.[Yi-Ping], Dong, Y.H.[Yu-Han], Yang, H.Z.[Hua-Zhong], Wang, Y.[Yu],
CLOSE: Curriculum Learning on the Sharing Extent Towards Better One-Shot NAS,
ECCV22(XX:578-594).
Springer DOI 2211
BibRef

Yun, J.[Juseung], Lee, J.[Janghyeon], Shon, H.[Hyounguk], Yi, E.[Eojindl], Kim, S.H.[Seung Hwan], Kim, J.[Junmo],
On the Angular Update and Hyperparameter Tuning of a Scale-Invariant Network,
ECCV22(XII:121-136).
Springer DOI 2211
BibRef

Dutson, M.[Matthew], Li, Y.[Yin], Gupta, M.[Mohit],
Event Neural Networks,
ECCV22(XI:276-293).
Springer DOI 2211
BibRef

Utasi, Á.[Ákos],
PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations,
ECV22(2797-2805)
IEEE DOI 2210
Training, Image segmentation, Semantics, Neural networks, Network architecture BibRef

Ding, X.H.[Xiao-Han], Zhang, X.Y.[Xiang-Yu], Han, J.G.[Jun-Gong], Ding, G.G.[Gui-Guang],
Scaling Up Your Kernels to 31X31: Revisiting Large Kernel Design in CNNs,
CVPR22(11953-11965)
IEEE DOI 2210
Convolutional codes, Shape, Scalability, Transformers, Data models, Convolutional neural networks, Deep learning architectures and techniques BibRef

Lin, F.Q.[Fan-Qing], Price, B.[Brian], Martinez, T.[Tony],
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks,
CVPR22(763-772)
IEEE DOI 2210
Deep learning, Image segmentation, Visualization, Shape, Semantics, Estimation, Deep learning architectures and techniques, Vision applications and systems BibRef

Liu, C.J.[Chuan-Jian], Han, K.[Kai], Xiao, A.[An], Nie, Y.[Ying], Zhang, W.[Wei], Wang, Y.H.[Yun-He],
Network Amplification with Efficient MACs Allocation,
NAS22(1932-1941)
IEEE DOI 2210
Statistical analysis, Computational modeling, Heuristic algorithms, Neural networks, Network architecture, Dynamic programming BibRef

Courtois, A.[Adrien], Morel, J.M.[Jean-Michel], Arias, P.[Pablo],
Investigating Neural Architectures by Synthetic Dataset Design,
VDU22(4886-4895)
IEEE DOI 2210
Systematics, Neural networks, Buildings, Estimation, Computer architecture BibRef

Chen, Y.[Ying], Mao, F.[Feng], Song, J.[Jie], Wang, X.C.[Xin-Chao], Wang, H.Q.[Hui-Qiong], Song, M.L.[Ming-Li],
Self-born Wiring for Neural Trees,
ICCV21(5027-5036)
IEEE DOI 2203
Wiring, Deep learning, Representation learning, Greedy algorithms, Scalability, Neural networks, Explainable AI BibRef

Deng, C.[Congyue], Litany, O.[Or], Duan, Y.[Yueqi], Poulenard, A.[Adrien], Tagliasacchi, A.[Andrea], Guibas, L.J.[Leonidas J.],
Vector Neurons: A General Framework for SO(3)-Equivariant Networks,
ICCV21(12180-12189)
IEEE DOI 2203
Geometry, Shape, Neurons, Network architecture, Task analysis, 3D from multiview and other sensors BibRef

Liu, Y.Q.[Yu-Qiao], Tang, Y.[Yehui], Sun, Y.[Yanan],
Homogeneous Architecture Augmentation for Neural Predictor,
ICCV21(12229-12238)
IEEE DOI 2203
Training, Performance evaluation, Deep learning, Neural networks, Training data, Transforms, BibRef

Yuan, K.[Kun], Li, Q.Q.[Quan-Quan], Guo, S.P.[Shao-Peng], Chen, D.P.[Da-Peng], Zhou, A.[Aojun], Yu, F.W.[Feng-Wei], Liu, Z.W.[Zi-Wei],
Differentiable Dynamic Wirings for Neural Networks,
ICCV21(317-326)
IEEE DOI 2203
Wiring, Training, Costs, Computational modeling, Aggregates, Neural networks, Efficient training and inference methods BibRef

Hu, J.[Jie], Cao, L.J.[Liu-Juan], Tong, T.[Tong], Ye, Q.X.[Qi-Xiang], Zhang, S.C.[Sheng-Chuan], Li, K.[Ke], Huang, F.Y.[Fei-Yue], Shao, L.[Ling], Ji, R.R.[Rong-Rong],
Architecture Disentanglement for Deep Neural Networks,
ICCV21(652-661)
IEEE DOI 2203
Deep learning, Codes, Semantics, Neural networks, Network architecture, Explainable AI, Representation learning BibRef

Jeevan, P.[Pranav], Sethi, A.[Amit],
Resource-efficient Hybrid X-formers for Vision,
WACV22(3555-3563)
IEEE DOI 2202
Computational modeling, Memory management, Graphics processing units, Training data, Transformers, Scene Understanding BibRef

Yu, T.[Tan], Li, X.[Xu], Cai, Y.F.[Yun-Feng], Sun, M.M.[Ming-Ming], Li, P.[Ping],
S2-MLP: Spatial-Shift MLP Architecture for Vision,
WACV22(3615-3624)
IEEE DOI 2202
Training, Visualization, Image recognition, Convolution, Transformers, Deep Learning vision architecture BibRef

Gong, X.Y.[Xin-Yu], Chen, W.Y.[Wu-Yang], Chen, T.L.[Tian-Long], Wang, Z.Y.[Zhang-Yang],
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity,
WACV22(2957-2967)
IEEE DOI 2202
Code, Normalization.
WWW Link. Build on DARTS. Training, Codes, Image synthesis, Semisupervised learning, Data models, Robustness, Deep Learning BibRef

Henmi, T.[Takahiko], Zara, E.R.R.[Esmeraldo Ronnie Rey], Hirohashi, Y.[Yoshihiro], Kato, T.[Tsuyoshi],
Adaptive Signal Variances: CNN Initialization Through Modern Architectures,
ICIP21(374-378)
IEEE DOI 2201
Adaptation models, Convolution, Image processing, Stability analysis, Convolutional neural networks, Standards, Initialization BibRef

Gupta, S.[Shashank], Robles-Kelly, A.[Antonio],
Feature-Extracting Functions for Neural Logic Rule Learning,
DICTA20(1-2)
IEEE DOI 2201
Domain knowledge into behavior of neural network. Training, Knowledge engineering, Sentiment analysis, Neural networks, Programming, Feature extraction, rule learning BibRef

Imamura, A.[Akihiro], Arizumi, N.[Nana],
Gabor Filter Incorporated CNN for Compression,
IVCNZ21(1-5)
IEEE DOI 2201
Gabor in early layers of CNN. Image coding, Dictionaries, Costs, Convolution, Real-time systems, Gabor filters, compression, CNN, Gabor BibRef

Chen, Z.D.[Zhen-Duo], Liu, F.[Feng], Zhao, Z.L.[Zheng-Lai],
Let Them Choose What They Want: A Multi-Task CNN Architecture Leveraging Mid-Level Deep Representations for Face Attribute Classification,
ICIP21(879-883)
IEEE DOI 2201
Correlation, Image processing, Task analysis, Faces, deep learning, multi-task learning, attention BibRef

Zhang, K.[Kaihua], Dong, M.L.[Ming-Liang], Liu, B.[Bo], Yuan, X.T.[Xiao-Tong], Liu, Q.S.[Qing-Shan],
DeepACG: Co-Saliency Detection via Semantic-aware Contrast Gromov-Wasserstein Distance,
CVPR21(13698-13707)
IEEE DOI 2111
Image segmentation, Correlation, Image edge detection, Semantics, Network architecture, Benchmark testing BibRef

Gao, S.H.[Shang-Hua], Han, Q.[Qi], Li, D.[Duo], Cheng, M.M.[Ming-Ming], Peng, P.[Pai],
Representative Batch Normalization with Feature Calibration,
CVPR21(8665-8675)
IEEE DOI 2111
Training, Costs, Neural networks, Standardization, Calibration BibRef

Cui, Y.F.[Yu-Fei], Liu, Z.Q.[Zi-Quan], Li, Q.[Qiao], Chan, A.B.[Antoni B.], Xue, C.J.[Chun Jason],
Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression,
CVPR21(2392-2401)
IEEE DOI 2111
Training, Uncertainty, Computational modeling, Neural networks, Data models, Bayes methods BibRef

Li, Y.[Yawei], Li, W.[Wen], Danelljan, M.[Martin], Zhang, K.[Kai], Gu, S.H.[Shu-Hang], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures,
CVPR21(2144-2153)
IEEE DOI 2111
Training, Visualization, Protocols, Computational modeling, Network architecture BibRef

Peer, D.[David], Stabinger, S.[Sebastian], Rodríguez-Sánchez, A.[Antonio],
Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks,
WACV21(256-265)
IEEE DOI 2106
Training, Measurement, Adaptation models, Neural networks, Memory management BibRef

Alt, T.[Tobias], Peter, P.[Pascal], Weickert, J.[Joachim], Schrader, K.[Karl],
Translating Numerical Concepts for PDEs into Neural Architectures,
SSVM21(294-306).
Springer DOI 2106
BibRef

Ruiz, A.[Adria], Agudo, A.[Antonio], Moreno-Noguer, F.[Francesc],
Generating Attribution Maps with Disentangled Masked Backpropagation,
ICCV21(885-894)
IEEE DOI 2203
Backpropagation, Visualization, Computational modeling, Piecewise linear approximation, Neural networks, BibRef

La Grassa, R.[Riccardo], Gallo, I.[Ignazio], Landro, N.[Nicola],
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task,
CAIP21(I:393-402).
Springer DOI 2112
BibRef

Yuan, Q.W.[Qiong-Wen], He, J.W.[Jing-Wei], Yu, L.[Lei], Zheng, G.[Gang],
AIM-Net: Bring Implicit Euler to Network Design,
ICIP20(1926-1930)
IEEE DOI 2011
Neural networks, Adaptation models, Convergence, Mathematical model, Image resolution, Signal resolution, image superresolution BibRef

Murdock, C.[Calvin], Lucey, S.[Simon],
Dataless Model Selection With the Deep Frame Potential,
CVPR20(11254-11262)
IEEE DOI 2008
Dictionaries, Sparse representation, Robustness, Coherence, Neural networks, Machine learning BibRef

Radosavovic, I.[Ilija], Kosaraju, R.P.[Raj Prateek], Girshick, R.[Ross], He, K.[Kaiming], Dollár, P.[Piotr],
Designing Network Design Spaces,
CVPR20(10425-10433)
IEEE DOI 2008
Computational modeling, Manuals, Tools, Sociology, Statistics, Training, Visualization BibRef

Zoran, D.[Daniel], Chrzanowski, M.[Mike], Huang, P.S.[Po-Sen], Gowal, S.[Sven], Mott, A.[Alex], Kohli, P.[Pushmeet],
Towards Robust Image Classification Using Sequential Attention Models,
CVPR20(9480-9489)
IEEE DOI 2008
Augment NN with attention model. Robustness, Computational modeling, Adaptation models, Brain modeling, Biological system modeling, Training BibRef

Huang, L.[Lei], Zhao, L.[Lei], Zhou, Y.[Yi], Zhu, F.[Fan], Liu, L.[Li], Shao, L.[Ling],
An Investigation Into the Stochasticity of Batch Whitening,
CVPR20(6438-6447)
IEEE DOI 2008
Training, Principal component analysis, Covariance matrices, Standardization, Optimization, Sociology BibRef

Lin, R., Liu, W., Liu, Z., Feng, C., Yu, Z., Rehg, J.M., Xiong, L., Song, L.,
Regularizing Neural Networks via Minimizing Hyperspherical Energy,
CVPR20(6916-6925)
IEEE DOI 2008
Neurons, Biological neural networks, Training, Optimization, Task analysis, Testing, Linear programming BibRef

Atzmon, M.[Matan], Lipman, Y.[Yaron],
SAL: Sign Agnostic Learning of Shapes From Raw Data,
CVPR20(2562-2571)
IEEE DOI 2008
Surface reconstruction, Shape, Neural networks, Interpolation, Mathematical model, Training BibRef

Tao, Y., Ma, R., Shyu, M., Chen, S.,
Challenges in Energy-Efficient Deep Neural Network Training with FPGA,
LPCV20(1602-1611)
IEEE DOI 2008
Field programmable gate arrays, Computational modeling, Training, Hardware, Neural networks, Machine learning, Graphics processing units BibRef

Banerjee, S., Chakraborty, S.,
Deepsub: A Novel Subset Selection Framework for Training Deep Learning Architectures,
ICIP19(1615-1619)
IEEE DOI 1910
Submodular optimization, Deep learning BibRef

Rashwan, A., Kalra, A., Poupart, P.,
Matrix Nets: A New Deep Architecture for Object Detection,
NeruArch19(2025-2028)
IEEE DOI 2004
learning (artificial intelligence), neural net architecture, object detection, Matrix Nets, deep architecture, object detection, neural architecture BibRef

Chen, H.T.[Han-Ting], Wang, Y.H.[Yun-He], Xu, C.[Chang], Yang, Z.H.[Zhao-Hui], Liu, C.J.[Chuan-Jian], Shi, B.X.[Bo-Xin], Xu, C.J.[Chun-Jing], Xu, C.[Chao], Tian, Q.[Qi],
Data-Free Learning of Student Networks,
ICCV19(3513-3521)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), neural net architecture, Knowledge engineering BibRef

Srinivas, S.[Suraj], Babu, V.[Venkatesh],
Learning Neural Network Architectures using Backpropagation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Hasegawa, R.[Ryoma], Hotta, K.[Kazuhiro],
PLSNet: A simple network using Partial Least Squares regression for image classification,
ICPR16(1601-1606)
IEEE DOI 1705
Convolution, Databases, Feature extraction, Image classification, Network architecture, Principal component analysis, Training, Convolutional Neural Network, Deep Learning, PCANet, PLSNet, Partial Least Squares Regression, Stacked, PLS BibRef

Elliman, D.G.[David G.], Youssef, S.M.[Sherin M.],
Contextual Swarm-Based Multi-layered Lattices: A New Architecture for Contextual Pattern Recognition,
DAS04(496-507).
Springer DOI 0505
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Architecture, Neural Architecture Search, NAS .