Searching for Robust Binary Neural Networks via Bimodal Parameter Perturbation

Abstract

Binary neural networks (BNNs) are advantageous in performance and memory footprint but suffer from low accuracy due to their limited expression capability. Recent works have tried to enhance the accuracy of BNNs via a gradient-based search algorithm and showed promising results. However, the mixture of architecture search and binarization induce the instability of the search process, resulting in convergence to the suboptimal point. To address this issue, we propose a BNN architecture search framework with bimodal parameter perturbation. The bimodal parameter perturbation can improve the stability of gradient-based architecture search by reducing the sharpness of the loss surface along both weight and architecture parameter axes. In addition, we refine the inverted bottleneck convolution block for having robustness with BNNs. The synergy of the refined space and the stabilized search process allows us to find out the accurate BNNs with high computation efficiency. Experimental results show that our framework finds the best architecture on CIFAR-100 and ImageNet datasets in the existing search space for BNNs. We also tested our framework on another search space based on the inverted bottleneck convolution block, and the selected BNN models using our approach achieved the highest accuracy on both datasets with a much smaller number of equivalent operations than previous works.