There is no abstract available for this article.
This work was supported by National Natural Science Foundation of China (NSFC) (Grant Nos. 61751306, 61921006). The author wants to thank Shen-Huan LYU and Zhi-Hao TAN for discussion and help in figures.
[1] Neyshabur B, Tomioka R, Srebro N. Norm-based capacity control in neural networks. In: Proceedings of the 28th Conference on Learing Theory, Paris, 2015. 1376--1401. Google Scholar
[2] Zhang C Y, Bengio S, Hardt M, et al. Understanding deep learning requires rethinking generalization. In: Proceedings of the 5th International Conference on Learning Representation, Toulon, 2017. Google Scholar
[3] Nagarajan V, Kolter J Z. Uniform convergence may be unable to explain generalization in deep learning. In: Proceedins of Advances in Neural Information Processing Systems, 2019. 11615--11626. Google Scholar
[4] Lawrence S, Giles C L, Tsoi A C. Lessons in neural network training: overfitting may be harder than expected. In: Proceedings of the 14th National Conference on Artificial Intelligence, Providence, 1997. 540--545. Google Scholar
[5] Liu Y Y, Starzyk J A, Zhu Z. Optimized Approximation Algorithm in Neural Networks Without Overfitting. IEEE Trans Neural Netw, 2008, 19: 983-995 CrossRef Google Scholar
[6] Kulis B. Metric learning: a survey. Found Trends Mach Learn, 2013, 5: 287--363. Google Scholar
[7] Davis J V, Kulis B, Jain P, et al. Information-theoretic metric learning. In: Proceedings of the 24th International Conference on Machine Learning, Corvalis, 2007. 209--216. Google Scholar
Figure 1
(Color online) (a) A decompositional view of deep neural networks; (b) a typical performance plot showing that over-parameterization of the CC part can lead to overfitting (replot based on experimental results presented in