Paper Info
Title
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets.
Abstract
This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational complexity. Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high. Therefore, the conventional sign function cannot be well used for effectively binarizing full-precision values in BNNs. To this end, we present a simple yet effective approach called AdaBin to adaptively obtain the optimal binary sets \(\{b_1, b_2\}\) (\(b_1, b_2\in \mathbb {R}\)) of weights and activations for each layer instead of a fixed set (i.e., \(\{-1, +1\}\)). In this way, the proposed method can better fit different distributions and increase the representation ability of binarized features. In practice, we use the center position and distance of 1-bit values to define a new binary quantization function. For the weights, we propose an equalization method to align the symmetrical center of binary distribution to real-valued distribution, and minimize the Kullback-Leibler divergence of them. Meanwhile, we introduce a gradient-based optimization method to get these two parameters for activations, which are jointly trained in an end-to-end manner. Experimental results on benchmark models and datasets demonstrate that the proposed AdaBin is able to achieve state-of-the-art performance. For instance, we obtain a 66.4% Top-1 accuracy on the ImageNet using ResNet-18 architecture, and a 69.4 mAP on PASCAL VOC using SSD300.
Year
DOI
Venue
2022
10.1007/978-3-031-20083-0_23
European Conference on Computer Vision
Keywords
DocType
Citations 
Binary neural networks,Adaptive binary sets
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zhijun Tu100.34
Xinghao Chen200.34
Pengju Ren300.34
Yunhe Wang411322.76