Discovering Parametric Activation Functions

Abstract

Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation functions have been inconsistent and task dependent, and therefore the rectified linear unit (ReLU) is still the most commonly used. This paper proposes a technique for customizing activation functions automatically, resulting in reliable improvements in performance. Evolutionary search is used to discover the general form of the function, and gradient descent to optimize its parameters for different parts of the network and over the learning process. Experiments with four different neural network architectures on the CIFAR-10 and CIFAR-100 image classification datasets show that this approach is effective. It discovers both general activation functions and specialized functions for different architectures, consistently improving accuracy over ReLU and other activation functions by significant margins. The approach can therefore be used as an automated optimization step in applying deep learning to new tasks.

Published in Neural Networks. 34 pages, 10 figures, 11 tables

References in corpus (9)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Improving neural networks by preventing co-adaptation of feature detectors
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Striving for Simplicity: The All Convolutional Net
• Learning Activation Functions to Improve Deep Neural Networks
• A Survey on Neural Architecture Search
• Evolving Normalization-Activation Layers
• Learning Activation Functions: A new paradigm for understanding Neural Networks
• Regularized Evolutionary Population-Based Training

Cited by in corpus (1)

• Review: Deep Learning in Electron Microscopy