Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks

• URL: http://arxiv.org/abs/2307.01649v2
• Date: Sun, 18 Feb 2024 03:29:20 GMT
• Title: Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks
• Authors: Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang
• Abstract summary: We study the performance of ConvResNeXts, trained with weight decay from the perspective of nonparametric classification. Our analysis allows for infinitely many building blocks in ConvResNeXts, and shows that weight decay implicitly enforces sparsity on these blocks.
• Score: 82.03459331544737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional residual neural networks (ConvResNets), though overparameterized, can achieve remarkable prediction performance in practice, which cannot be well explained by conventional wisdom. To bridge this gap, we study the performance of ConvResNeXts, which cover ConvResNets as a special case, trained with weight decay from the perspective of nonparametric classification. Our analysis allows for infinitely many building blocks in ConvResNeXts, and shows that weight decay implicitly enforces sparsity on these blocks. Specifically, we consider a smooth target function supported on a low-dimensional manifold, then prove that ConvResNeXts can adapt to the function smoothness and low-dimensional structures and efficiently learn the function without suffering from the curse of dimensionality. Our findings partially justify the advantage of overparameterized ConvResNeXts over conventional machine learning models.

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