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The choice of activation functions is crucial for modern deep neuralnetworks. Popular hand-designed activation functions like Rectified LinearUnit(ReLU) and its variants show promising performance in various tasks andmodels. Swish, the automatically discovered activation function, has beenproposed and outperforms ReLU on many challenging datasets. However, it has twomain drawbacks. First, the tree-based search space is highly discrete andrestricted, which is difficult for searching. Second, the sample-basedsearching method is inefficient, making it infeasible to find specializedactivation functions for each dataset or neural architecture. To tackle thesedrawbacks, we propose a new activation function called Piecewise LinearUnit(PWLU), which incorporates a carefully designed formulation and learningmethod. It can learn specialized activation functions and achieves SOTAperformance on large-scale datasets like ImageNet and COCO. For example, onImageNet classification dataset, PWLU improves 0.9%/0.53%/1.0%/1.7%/1.0% top-1accuracy over Swish forResNet-18/ResNet-50/MobileNet-V2/MobileNet-V3/EfficientNet-B0. PWLU is alsoeasy to implement and efficient at inference, which can be widely applied inreal-world applications.

Learning specialized activation functions with the Piecewise Linear Unit