# Searching for Accurate Binary Neural Architectures

arXiv: https://arxiv.org/abs/1909.07378 · HTML: https://mlsanity.com/paper/1909.07378/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 13 of 13)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- ["BNN - BN = ?": Training Binary Neural Networks without Batch Normalization](https://mlsanity.com/paper/2104.08215/) (2021)
- [GhostNets on Heterogeneous Devices via Cheap Operations](https://mlsanity.com/paper/2201.03297/) (2022)
- [Enabling Binary Neural Network Training on the Edge](https://mlsanity.com/paper/2102.04270/) (2021)
- [GhostSR: Learning Ghost Features for Efficient Image Super-Resolution](https://mlsanity.com/paper/2101.08525/) (2021)
- [BNAS v2: Learning Architectures for Binary Networks with Empirical Improvements](https://mlsanity.com/paper/2110.08562/) (2021)
- [Compacting Binary Neural Networks by Sparse Kernel Selection](https://mlsanity.com/paper/2303.14470/) (2023)
- [The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention](https://mlsanity.com/paper/2405.17776/) (2024)
- [Differentiable Architecture Pruning for Transfer Learning](https://mlsanity.com/paper/2107.03375/) (2021)
- [Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey](https://mlsanity.com/paper/2208.10498/) (2022)
- [TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search](https://mlsanity.com/paper/2310.09525/) (2023)
- [An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition](https://mlsanity.com/paper/2403.18208/) (2024)

## Abstract (arXiv)

Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and features, their accuracy is usually much lower than that of full-precision (i.e. 32-bit) models. Here we present a new frame work for automatically searching for compact but accurate binary neural networks. In practice, number of channels in each layer will be encoded into the search space and optimized using the evolutionary algorithm. Experiments conducted on benchmark datasets and neural architectures demonstrate that our searched binary networks can achieve the performance of full-precision models with acceptable increments on model sizes and calculations.