Activate or Not: Learning Customized Activation
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Modern activation layers use non-linear functions to activate the neurons. In this paper, we present a simple but effective activation function we term ACON which learns to activate the neurons or not. Surprisingly, we find Swish, the recent popular NAS-searched activation, can be interpreted as a smooth approximation to ReLU. Intuitively, in the same way, we approximate the variants in the ReLU family to the Swish family, we call ACON, which makes Swish a special case of ACON and remarkably improves the performance. Next, we present meta-ACON, which explicitly learns to optimize the parameter switching between non-linear (activate) and linear (inactivate) and provides a new design space. By simply changing the activation function, we improve the ImageNet top-1 accuracy rate by 6.7 respectively.
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