mlsanityData to 7 Oct 2026

FTSO: Effective NAS via First Topology Second Operator

arXiv 2303.12948 ↗First posted 28 Feb 2023cs.CV cs.LGcited by 3

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Abstract (arXiv)

Existing one-shot neural architecture search (NAS) methods have to conduct a search over a giant super-net, which leads to the huge computational cost. To reduce such cost, in this paper, we propose a method, called FTSO, to divide the whole architecture search into two sub-steps. Specifically, in the first step, we only search for the topology, and in the second step, we search for the operators. FTSO not only reduces NAS's search time from days to 0.68 seconds, but also significantly improves the found architecture's accuracy. Our extensive experiments on ImageNet show that within 18 seconds, FTSO can achieve a 76.4% testing accuracy, 1.5% higher than the SOTA, PC-DARTS. In addition, FTSO can reach a 97.77% testing accuracy, 0.27% higher than the SOTA, with nearly 100% (99.8%) search time saved, when searching on CIFAR10.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 25 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

19 of 25 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2014Going Deeper with Convolutionscited by 4,231
  5. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  6. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  7. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  8. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  9. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  10. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  11. 2017Progressive Neural Architecture Searchcited by 483
  12. 2018SNAS: Stochastic Neural Architecture Searchcited by 288
7 more
  1. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 277
  2. 2019PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Searchcited by 265
  3. 2019Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluationcited by 230
  4. 2019Understanding and Robustifying Differentiable Architecture Searchcited by 176
  5. 2020NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Sizecited by 98
  6. 2019BayesNAS: A Bayesian Approach for Neural Architecture Searchcited by 67
  7. 2019Understanding Architectures Learnt by Cell-based Neural Architecture Searchcited by 48

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Cited by year

Citing papers per year: 2023: 3.

3 papers.

  1. 2023DisWOT: Student Architecture Search for Distillation WithOut Trainingcited by 29
  2. 2023Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevancecited by 10
  3. 2023Bootstrap The Original Latent: Learning a Private Model from a Black-box Modelcited by 1