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SAU: Smooth Activation Function Using Convolution with Approximate Identities

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Computer Vision – ECCV 2022 (ECCV 2022)

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 5.63%, 2.95%, and 2.50% improvement with ShuffleNet V2 (2.0x), PreActResNet 50 and ResNet 50 models respectively on the CIFAR100 dataset and 2.31% improvement with ShuffleNet V2 (1.0x) model on ImageNet-1k dataset.

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Acknowledgment

The authors are very grateful to Dr. Bapi Chatterjee for lending GPU equipment which helped in carrying out some of the important experiments.

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Correspondence to Koushik Biswas .

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Biswas, K., Kumar, S., Banerjee, S., Kumar Pandey, A. (2022). SAU: Smooth Activation Function Using Convolution with Approximate Identities. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13681. Springer, Cham. https://doi.org/10.1007/978-3-031-19803-8_19

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  • DOI: https://doi.org/10.1007/978-3-031-19803-8_19

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