GAAF: Searching Activation Functions for Binary Neural Networks Through Genetic Algorithm

Abstract

Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identified through our GA-based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Interestingly, periodic functions are identified as a key component for most of the discovered AFs, which rarely exist in human designed AFs. Our method offers a novel approach for designing general and application-specific BNN architecture. GAAF will be released on GitHub.

Evaluation of maturity and moisture content in fresh jujube using fractal theory and tissue images

“…Image binarization is to set the gray value of the pixels on the image to 0 or 255, that is, to present the entire image with an obvious black-and-white color. Through binarization processing, the image becomes simple, the amount of data is reduced, and the target of interest can be highlighted (Li et al, 2023). The binarization process requires threshold setting.…”