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International Journal of Computer Vision

Erschienen in:

01.12.2021

Towards Compact 1-bit CNNs via Bayesian Learning

verfasst von: Junhe Zhao, Sheng Xu, Baochang Zhang, Jiaxin Gu, David Doermann, Guodong Guo

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2022

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Abstract

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.

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Titel: Towards Compact 1-bit CNNs via Bayesian Learning
verfasst von: Junhe Zhao
Sheng Xu
Baochang Zhang
Jiaxin Gu
David Doermann
Guodong Guo
Publikationsdatum: 01.12.2021
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2022
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-021-01543-y

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