# Binary Neural Networks: A Survey

Source: [Binary Neural Networks: A Survey](<https://www.sota2.com/research/paper/68fa67511e209131cea156b0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:11:14.110Z

Authors: Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe

Published: 2020-03-31T16:47:20.000Z

## Abstract

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on ImageNet](<https://www.sota2.com/research/sota/image-classification-on-imagenet.md>): Image Classification; ImageNet; Top-1 Accuracy: 70.4.

- [Image Classification on ImageNet 1k (val)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-1k-val.md>): Image Classification; ImageNet-1k (val); Top-1 Accuracy: 72.8.

- [Image Classification on ImageNet (val)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-val.md>): Image Classification; ImageNet (val); Top-1 Acc: 62.9.

- [Image Classification on CIFAR-10 (test)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test.md>): Image Classification; CIFAR-10 (test); Accuracy: not recorded.

- [Image Classification on CIFAR-10](<https://www.sota2.com/research/sota/image-classification-on-cifar-10.md>): Image Classification; CIFAR-10; Accuracy: not recorded.

- [Image Classification on ImageNet (test)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-test.md>): Image Classification; ImageNet (test); Top-1 Acc: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.03333>)

[PDF](<https://arxiv.org/pdf/2004.03333.pdf>)