Towards Compact 1-bit CNNs via Bayesian Learning

摘要

Deep convolutional neural networks (DCNNs) have dominated as the best performers on almost all computer vision tasks over the past several years. However, it remains a major challenge to deploy these powerful DCNNs in resource-limited environments, such as embedded devices and smartphones. To this end, 1-bit CNNs have emerged as a feasible solution as they are much more resource-efficient. Unfortunately, they often suffer from a significant performance drop compared to their full-precision counterparts. In this paper, we propose a novel Bayesian Optimized compact 1-bit CNNs (BONNs) model, which has the advantage of Bayesian learning, to improve the performance of 1-bit CNNs significantly. BONNs incorporate the prior distributions of full-precision kernels, features, and filters into a Bayesian framework to construct 1-bit CNNs in a comprehensive end-to-end manner. The proposed Bayesian learning algorithms are well-founded and used to optimize the network simultaneously in different kernels, features, and filters, which largely improves the compactness and capacity of 1-bit CNNs. We further introduce a new Bayesian learning-based pruning method for 1-bit CNNs, which significantly increases the model efficiency with very competitive performance. This enables our method to be used in a variety of practical scenarios. Extensive experiments on the ImageNet, CIFAR, and LFW datasets show that BONNs achieve the best in classification performance compared to a variety of state-of-the-art 1-bit CNN models. In particular, BONN achieves a strong generalization performance on the object detection task.