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arXiv · 2109.13210

SAU: Smooth activation function using convolution with approximate identities

Koushik Biswas·Sandeep Kumar·Shilpak Banerjee·Ashish Kumar Pandey
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Abstract

Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin. Over the years, many smooth approximations of ReLU have been proposed using various smoothing techniques. We propose new smooth approximations of a non-differentiable activation function by convolving it with approximate identities. In particular, we present smooth approximations of Leaky ReLU and show that they outperform several well-known activation functions in various datasets and models. We call this function Smooth Activation Unit (SAU). Replacing ReLU by SAU, we get 5.12% improvement with ShuffleNet V2 (2.0x) model on CIFAR100 dataset.

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Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey. 2021-09-27. SAU: Smooth activation function using convolution with approximate identities. https://arxiv.org/abs/2109.13210

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