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

SAU: Smooth activation function using convolution with approximate identities