# Binarized Neural Networks on the ImageNet Classification Task

arXiv: https://arxiv.org/abs/1604.03058 · HTML: https://mlsanity.com/paper/1604.03058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric Quantizer](https://mlsanity.com/paper/2104.00210/) (2021)
- [Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising](https://mlsanity.com/paper/2209.08759/) (2022)

## Abstract (arXiv)

We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance. With a moderate size network of 13 layers, we obtained top-5 classification accuracy rate of 84.1 % on validation set through network distillation, much better than previous published results of 73.2% on XNOR network and 69.1% on binarized GoogleNET. We expect networks of better performance can be obtained by following our current strategies. We provide a detailed discussion and preliminary analysis on strategies used in the network training.