Nov 28, 2022
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Human neurons and neurotransmission mechanisms have been realized in a deep neural network by the theoretical implementations of activation functions. However, recent studies have reported that the threshold potential of neurons exhibited different values by locations and types of individual neurons, and the activation functions have limitations in representing it. Therefore, this study proposes a simple yet effective activation function that exhibits different thresholds and adaptive activations according to the positions of units and the contexts of inputs, and we denoted it as ASH activation function. ASH highlights informative features, which exhibit large values in the top percentiles in an input, whereas it rectifies low values. Most importantly, ASH exhibits trainable, adaptive, and context-aware properties compared to other activation functions. To validate the effectiveness and robustness of ASH, we implemented ASH into many deep learning models for various tasks, including classification, detection, segmentation, and image generation. The experimental analysis demonstrates that our activation function exhibits outstanding performance in any deep learning applications.Human neurons and neurotransmission mechanisms have been realized in a deep neural network by the theoretical implementations of activation functions. However, recent studies have reported that the threshold potential of neurons exhibited different values by locations and types of individual neurons, and the activation functions have limitations in representing it. Therefore, this study proposes a simple yet effective activation function that exhibits different thresholds and adaptive activation…
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