# Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

arXiv: https://arxiv.org/abs/1811.10413 · HTML: https://mlsanity.com/paper/1811.10413/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2018.

## Built on by (most cited first, 30 of 37)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
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- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [Distribution-sensitive Information Retention for Accurate Binary Neural Network](https://mlsanity.com/paper/2109.12338/) (2021)
- [Sharpness-aware Quantization for Deep Neural Networks](https://mlsanity.com/paper/2111.12273/) (2021)
- [EcoFormer: Energy-Saving Attention with Linear Complexity](https://mlsanity.com/paper/2209.09004/) (2022)
- [Hard Sample Matters a Lot in Zero-Shot Quantization](https://mlsanity.com/paper/2303.13826/) (2023)
- [Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization](https://mlsanity.com/paper/2109.00212/) (2021)
- [Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices](https://mlsanity.com/paper/2101.04446/) (2021)
- [ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization](https://mlsanity.com/paper/2203.03610/) (2022)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [Single-path Bit Sharing for Automatic Loss-aware Model Compression](https://mlsanity.com/paper/2101.04935/) (2021)
- [Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators](https://mlsanity.com/paper/2101.11748/) (2021)
- [Self-Distribution Binary Neural Networks](https://mlsanity.com/paper/2103.02394/) (2021)
- [GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples](https://mlsanity.com/paper/2305.07931/) (2023)
- [Improving Binary Neural Networks through Fully Utilizing Latent Weights](https://mlsanity.com/paper/2110.05850/) (2021)
- [BNAS v2: Learning Architectures for Binary Networks with Empirical Improvements](https://mlsanity.com/paper/2110.08562/) (2021)
- [Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm](https://mlsanity.com/paper/2203.11323/) (2022)
- [BOLD: Boolean Logic Deep Learning](https://mlsanity.com/paper/2405.16339/) (2024)
- [The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention](https://mlsanity.com/paper/2405.17776/) (2024)
- [Training Multi-bit Quantized and Binarized Networks with A Learnable Symmetric Quantizer](https://mlsanity.com/paper/2104.00210/) (2021)
- [Binarizing by Classification: Is soft function really necessary?](https://mlsanity.com/paper/2205.07433/) (2022)
- [BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation](https://mlsanity.com/paper/2207.01394/) (2022)
- [E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information Flow](https://mlsanity.com/paper/2207.06893/) (2022)
- [RepBNN: towards a precise Binary Neural Network with Enhanced Feature Map via Repeating](https://mlsanity.com/paper/2207.09049/) (2022)
- [Binary Neural Networks as a general-propose compute paradigm for on-device computer vision](https://mlsanity.com/paper/2202.03716/) (2022)
- [How to train accurate BNNs for embedded systems?](https://mlsanity.com/paper/2206.12322/) (2022)

## Abstract (arXiv)

In this paper, we propose to train convolutional neural networks (CNNs) with both binarized weights and activations, leading to quantized models specifically for mobile devices with limited power capacity and computation resources. Previous works on quantizing CNNs seek to approximate the floating-point information using a set of discrete values, which we call value approximation, but typically assume the same architecture as the full-precision networks. In this paper, however, we take a novel 'structure approximation' view for quantization---it is very likely that a different architecture may be better for best performance. In particular, we propose a `network decomposition' strategy, named Group-Net, in which we divide the network into groups. In this way, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. In addition, we learn effective connections among groups to improve the representational capability. Moreover, the proposed Group-Net shows strong generalization to other tasks. For instance, we extend Group-Net for highly accurate semantic segmentation by embedding rich context into the binary structure. Experiments on both classification and semantic segmentation tasks demonstrate the superior performance of the proposed methods over various popular architectures. In particular, we outperform the previous best binary neural networks in terms of accuracy and major computation savings.