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arXiv · 2112.12299

A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization

Enrico Civitelli·Alessio Sortino·Matteo Lapucci·Francesco Bagattini·Giulio Galvan
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Abstract

Batch Normalization is an essential component of all state-of-the-art neural networks architectures. However, since it introduces many practical issues, much recent research has been devoted to designing normalization-free architectures. In this paper, we show that weights initialization is key to train ResNet-like normalization-free networks. In particular, we propose a slight modification to the summation operation of a block output to the skip-connection branch, so that the whole network is correctly initialized. We show that this modified architecture achieves competitive results on CIFAR-10, CIFAR-100 and ImageNet without further regularization nor algorithmic modifications.

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Enrico Civitelli, Alessio Sortino, Matteo Lapucci, Francesco Bagattini, Giulio Galvan. 2021-12-23. A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization. https://doi.org/10.1109/tnnls.2023.3325541

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