Computer Science > Machine Learning
[Submitted on 5 Mar 2023 (v1), last revised 7 Mar 2023 (this version, v2)]
Title:Reparameterization through Spatial Gradient Scaling
View PDFAbstract:Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training. However, there exists a gap in understanding how reparameterization may change and benefit the learning process of neural networks. In this paper, we present a novel spatial gradient scaling method to redistribute learning focus among weights in convolutional networks. We prove that spatial gradient scaling achieves the same learning dynamics as a branched reparameterization yet without introducing structural changes into the network. We further propose an analytical approach that dynamically learns scalings for each convolutional layer based on the spatial characteristics of its input feature map gauged by mutual information. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that without searching for reparameterized structures, our proposed scaling method outperforms the state-of-the-art reparameterization strategies at a lower computational cost.
Submission history
From: Alexander Detkov [view email][v1] Sun, 5 Mar 2023 17:57:33 UTC (2,645 KB)
[v2] Tue, 7 Mar 2023 02:07:01 UTC (2,645 KB)
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