# Binarized Neural Architecture Search

arXiv: https://arxiv.org/abs/1911.10862 · HTML: https://mlsanity.com/paper/1911.10862/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [ReCU: Reviving the Dead Weights in Binary Neural Networks](https://mlsanity.com/paper/2103.12369/) (2021)
- [BNAS v2: Learning Architectures for Binary Networks with Empirical Improvements](https://mlsanity.com/paper/2110.08562/) (2021)
- [Signed Binary Weight Networks](https://mlsanity.com/paper/2211.13838/) (2022)
- [Partial Binarization of Neural Networks for Budget-Aware Efficient Learning](https://mlsanity.com/paper/2211.06739/) (2022)
- [DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs](https://mlsanity.com/paper/2306.15390/) (2023)
- [QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks](https://mlsanity.com/paper/2605.22351/) (2026)

## Abstract (arXiv)

Neural architecture search (NAS) can have a significant impact in computer vision by automatically designing optimal neural network architectures for various tasks. A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models. Unfortunately, this area remains largely unexplored. BNAS is more challenging than NAS due to the learning inefficiency caused by optimization requirements and the huge architecture space. To address these issues, we introduce channel sampling and operation space reduction into a differentiable NAS to significantly reduce the cost of searching. This is accomplished through a performance-based strategy used to abandon less potential operations. Two optimization methods for binarized neural networks are used to validate the effectiveness of our BNAS. Extensive experiments demonstrate that the proposed BNAS achieves a performance comparable to NAS on both CIFAR and ImageNet databases. An accuracy of 96.53% vs. 97.22% is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a 40% faster search than the state-of-the-art PC-DARTS.