mlsanityData to 14 Oct 2026

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

arXiv 1603.05279 ↗First posted 16 Mar 2016cs.CVcited by 849

Not on the map. Why?

Cited by 33 in Knowledge distillation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.

Key facts. Cited by 849 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Mar 2016.

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Citing papers per year: 2021: 247; 2022: 186; 2023: 139; 2024: 111; 2025: 103; 2026: 63.

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