# Bayesian Optimized 1-Bit CNNs

## TL;DR

This paper introduces Bayesian optimized 1-bit CNNs (BONNs), a novel approach that leverages Bayesian learning to significantly enhance the performance of extremely low-precision neural networks for resource-constrained environments.

## Contribution

The paper proposes a new Bayesian framework for 1-bit CNNs that incorporates prior distributions and optimizes in both continuous and discrete spaces, improving performance.

## Key findings

- BONNs outperform state-of-the-art 1-bit CNNs on ImageNet and CIFAR datasets.
- The method effectively combines Bayesian losses to enhance model capacity.
- Extensive experiments validate the superior classification accuracy of BONNs.

## Abstract

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs in resource-limited environments, such as on embedded devices and smart phones. Researchers have realized that 1-bit CNNs can be one feasible solution to resolve the issue; however, they are baffled by the inferior performance compared to the full-precision DCNNs. In this paper, we propose a novel approach, called Bayesian optimized 1-bit CNNs (denoted as BONNs), taking the advantage of Bayesian learning, a well-established strategy for hard problems, to significantly improve the performance of extreme 1-bit CNNs. We incorporate the prior distributions of full-precision kernels and features into the Bayesian framework to construct 1-bit CNNs in an end-to-end manner, which have not been considered in any previous related methods. The Bayesian losses are achieved with a theoretical support to optimize the network simultaneously in both continuous and discrete spaces, aggregating different losses jointly to improve the model capacity. Extensive experiments on the ImageNet and CIFAR datasets show that BONNs achieve the best classification performance compared to state-of-the-art 1-bit CNNs.