14.5.9.8.17 Network Overfitting

Chapter Contents (Back)
Deep Nets. Overfitting. Learn too specific a description.

Liu, R.[Raymond], Gillies, D.F.[Duncan F.],
Overfitting in linear feature extraction for classification of high-dimensional image data,
PR(53), No. 1, 2016, pp. 73-86.
Elsevier DOI 1602
Dimensionality reduction BibRef

Xu, Y.F.[Yi-Feng], Wang, H.G.[Hui-Gang], Liu, X.[Xing], Sun's, W.T.[Wei-Tao],
An improved multi-branch residual network based on random multiplier and adaptive cosine learning rate method,
JVCIR(59), 2019, pp. 363-370.
Elsevier DOI 1903
Image classification, Residual network, Overfitting, Deep leaning, Batch size, Learning rate BibRef

Yang, J.[Jihai], Xiong, W.[Wei], Li, S.J.[Shi-Jun], Xu, C.[Chang],
Learning structured and non-redundant representations with deep neural networks,
PR(86), 2019, pp. 224-235.
Elsevier DOI 1811
Deep networks, Overfitting, Decorrelation BibRef

Cai, H.Y.[Hua-Yue], Zhang, X.[Xiang], Lan, L.[Long], Dong, G.H.[Guo-Hua], Xu, C.F.[Chuan-Fu], Liu, X.W.[Xin-Wang], Luo, Z.G.[Zhi-Gang],
Learning deep discriminative embeddings via joint rescaled features and log-probability centers,
PR(114), 2021, pp. 107852.
Elsevier DOI 2103
Deep discriminative embedding, Softmax loss, Easing overfitting BibRef

Li, L.[Lin], Spratling, M.W.[Michael W.],
Understanding and combating robust overfitting via input loss landscape analysis and regularization,
PR(136), 2023, pp. 109229.
Elsevier DOI 2301
Adversarial robustness, Adversarial training, Robust overfitting, Loss landscape analysis, Logit regularization BibRef

Shi, X.S.[Xiao-Shuang], Guo, Z.H.[Zhen-Hua], Li, K.[Kang], Liang, Y.[Yun], Zhu, X.F.[Xiao-Feng],
Self-paced resistance learning against overfitting on noisy labels,
PR(134), 2023, pp. 109080.
Elsevier DOI 2212
Convolutional neural networks, Self-paced resistance, Model overfitting, Noisy labels BibRef

Li, Z.[Zhuorong], Yu, D.[Daiwei], Wu, M.H.[Ming-Hui], Chan, S.[Sixian], Yu, H.[Hongchuan], Han, Z.[Zhike],
Revisiting single-step adversarial training for robustness and generalization,
PR(151), 2024, pp. 110356.
Elsevier DOI 2404
Adversarial robustness, Robust overfitting, Adversarial sample generation, Adversarial training, Pattern recognition BibRef

He, C.M.[Chun-Mei], Li, X.[Xiuguang], Xia, Y.[Yue], Tang, J.[Jing], Yang, J.[Jie], Ye, Z.C.[Zheng-Chun],
Addressing the Overfitting in Partial Domain Adaptation With Self-Training and Contrastive Learning,
CirSysVideo(34), No. 3, March 2024, pp. 1532-1545.
IEEE DOI 2403
Entropy, Feature extraction, Reliability, Adaptation models, Training, Cyberspace, Computer science, Transfer learning, contrastive learning BibRef


Tejero-de-Pablos, A.[Antonio],
Complementary-Contradictory Feature Regularization against Multimodal Overfitting,
WACV24(5667-5676)
IEEE DOI Code:
WWW Link. 2404
Visualization, Codes, Transfer learning, Semantics, Estimation, Manuals, Algorithms, Vision + language and/or other modalities BibRef

He, Z.B.[Zheng-Bao], Li, T.[Tao], Chen, S.[Sizhe], Huang, X.L.[Xiao-Lin],
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective,
AML23(2314-2321)
IEEE DOI 2309
BibRef

Grabinski, J.[Julia], Jung, S.[Steffen], Keuper, J.[Janis], Keuper, M.[Margret],
FrequencyLowCut Pooling: Plug and Play Against Catastrophic Overfitting,
ECCV22(XIV:36-57).
Springer DOI 2211
BibRef

Balasubramanian, S.[Sriram], Feizi, S.[Soheil],
Towards Improved Input Masking for Convolutional Neural Networks,
ICCV23(1855-1865)
IEEE DOI 2401
BibRef

Singla, V.[Vasu], Singla, S.[Sahil], Feizi, S.[Soheil], Jacobs, D.[David],
Low Curvature Activations Reduce Overfitting in Adversarial Training,
ICCV21(16403-16413)
IEEE DOI 2203
Training, Computational modeling, Neural networks, Robustness, Standards, Adversarial learning, BibRef

Yazici, Y., Foo, C.S., Winkler, S., Yap, K.H., Chandrasekhar, V.,
Empirical Analysis Of Overfitting And Mode Drop In GAN Training,
ICIP20(1651-1655)
IEEE DOI 2011
Training, Generators, Generative adversarial networks, Semantics, Noise measurement, Deep Learning BibRef

Huesmann, K.[Karim], Rodriguez, L.G.[Luis Garcia], Linsen, L.[Lars], Risse, B.[Benjamin],
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks,
EDL-AI20(130-145).
Springer DOI 2103
BibRef

Webster, R.[Ryan], Rabin, J.[Julien], Simon, L.[Loic], Jurie, F.[Frederic],
Detecting Overfitting of Deep Generative Networks via Latent Recovery,
CVPR19(11265-11274).
IEEE DOI 2002
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Structural Description, Spatial Descriptions in Deep Networks .


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