mlsanityData to 7 Oct 2026

BATS: Binary ArchitecTure Search

arXiv 2003.01711 ↗First posted 3 Mar 2020cs.CV cs.LGcited by 26

Not on the map. Why?

Abstract (arXiv)

This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by means of Neural Architecture Search (NAS). We show that directly applying NAS to the binary domain provides very poor results. To alleviate this, we describe, to our knowledge, for the first time, the 3 key ingredients for successfully applying NAS to the binary domain. Specifically, we (1) introduce and design a novel binary-oriented search space, (2) propose a new mechanism for controlling and stabilising the resulting searched topologies, (3) propose and validate a series of new search strategies for binary networks that lead to faster convergence and lower search times. Experimental results demonstrate the effectiveness of the proposed approach and the necessity of searching in the binary space directly. Moreover, (4) we set a new state-of-the-art for binary neural networks on CIFAR10, CIFAR100 and ImageNet datasets. Code will be made available https://github.com/1adrianb/binary-nas

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 11; 2022: 7; 2023: 5; 2024: 3.

26 papers.

Sort:

  1. 2021Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Networkcited by 55
  2. 2021ReCU: Reviving the Dead Weights in Binary Neural Networkscited by 51
  3. 2021A comprehensive review of Binary Neural Networkcited by 44
  4. 2021BatchQuant: Quantized-for-all Architecture Search with Robust Quantizercited by 19
  5. 2024Lightweight Pixel Difference Networks for Efficient Visual Representation Learningcited by 17
  6. 2021Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networkscited by 14
  7. 2022Lipschitz Continuity Retained Binary Neural Networkcited by 14
  8. 2021Automated Deep Learning: Neural Architecture Search Is Not the Endcited by 13
  9. 2022Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networkscited by 13
  10. 2023Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superpositioncited by 8
  11. 2021BoolNet: Minimizing The Energy Consumption of Binary Neural Networkscited by 6
  12. 2022QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noisecited by 4
14 more
  1. 2021RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Thingscited by 3
  2. 2021Improving Binary Neural Networks through Fully Utilizing Latent Weightscited by 3
  3. 2021BNAS v2: Learning Architectures for Binary Networks with Empirical Improvementscited by 3
  4. 2022DASS: Differentiable Architecture Search for Sparse neural networkscited by 2
  5. 2021Unsupervised Representation Learning for Binary Networks by Joint Classifier Learningcited by 1
  6. 2022Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Surveycited by 1
  7. 2023Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysiscited by 1
  8. 2022ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networkscited by 0
  9. 2022LAB: Learnable Activation Binarizer for Binary Neural Networkscited by 0
  10. 2023DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNscited by 0
  11. 2023SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devicescited by 0
  12. 2023Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classificationcited by 0
  13. 2024Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Modelcited by 0
  14. 2024NAS-BNN: Neural Architecture Search for Binary Neural Networkscited by 0