mlsanityData to 7 Oct 2026

Core-set Sampling for Efficient Neural Architecture Search

arXiv 2107.06869 ↗First posted 8 Jul 2021cs.LG cs.NEcited by 23

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Abstract (arXiv)

Neural architecture search (NAS), an important branch of automatic machine learning, has become an effective approach to automate the design of deep learning models. However, the major issue in NAS is how to reduce the large search time imposed by the heavy computational burden. While most recent approaches focus on pruning redundant sets or developing new search methodologies, this paper attempts to formulate the problem based on the data curation manner. Our key strategy is to search the architecture using summarized data distribution, i.e., core-set. Typically, many NAS algorithms separate searching and training stages, and the proposed core-set methodology is only used in search stage, thus their performance degradation can be minimized. In our experiments, we were able to save overall computational time from 30.8 hours to 3.5 hours, 8.8x reduction, on a single RTX 3090 GPU without sacrificing accuracy.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 33 of its 49 references that are in the map. First posted on arXiv 8 Jul 2021.

Builds on

33 of 49 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  6. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  7. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  8. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  9. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 937
  10. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  11. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  12. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
21 more
  1. 2017Progressive Neural Architecture Searchcited by 483
  2. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  3. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 411
  4. 2017Large-Scale Evolution of Image Classifierscited by 348
  5. 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 289
  6. 2018SNAS: Stochastic Neural Architecture Searchcited by 288
  7. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 277
  8. 2019PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Searchcited by 265
  9. 2019Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentationcited by 258
  10. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
  11. 2019Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluationcited by 230
  12. 2017SMASH: One-Shot Model Architecture Search through HyperNetworkscited by 200
  13. 2017Hierarchical Representations for Efficient Architecture Searchcited by 188
  14. 2017Genetic CNNcited by 124
  15. 2019Evaluating the Search Phase of Neural Architecture Searchcited by 120
  16. 2016Coresets for Scalable Bayesian Logistic Regressioncited by 79
  17. 2019NAS-FCOS: Fast Neural Architecture Search for Object Detectioncited by 53
  18. 2019Small-GAN: Speeding Up GAN Training Using Core-setscited by 40
  19. 2018Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cellscited by 39
  20. 2019Efficient Neural Architecture Search via Proximal Iterationscited by 31
  21. 2020DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Searchcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 8; 2024: 3; 2025: 5; 2026: 2.

23 papers.

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  1. 2022DeepCore: A Comprehensive Library for Coreset Selection in Deep Learningcited by 146
  2. 2023Neural Architecture Search: Insights from 1000 Paperscited by 136
  3. 2023Finding Support Examples for In-Context Learningcited by 104
  4. 2023InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruningcited by 90
  5. 2023Dataset Quantizationcited by 47
  6. 2022Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamicscited by 26
  7. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  8. 2021A Tale Of Two Long Tailscited by 19
  9. 2022PASHA: Efficient HPO and NAS with Progressive Resource Allocationcited by 13
  10. 2024ELFS: Label-Free Coreset Selection with Proxy Training Dynamicscited by 9
  11. 2024GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruningcited by 8
  12. 2022Speeding up NAS with Adaptive Subset Selectioncited by 6
11 more
  1. 2025Coresets from Trajectories: Selecting Data via Correlation of Loss Differencescited by 3
  2. 2023Efficient Architecture Search via Bi-level Data Pruningcited by 2
  3. 2025PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Trainingcited by 2
  4. 2023Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Searchcited by 1
  5. 2024TED: Accelerate Model Training by Internal Generalizationcited by 1
  6. 2025Finding the Muses: Identifying Coresets through Loss Trajectoriescited by 1
  7. 2025DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classificationcited by 1
  8. 2023Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithmscited by 0
  9. 2025Offline Behavioral Data Selectioncited by 0
  10. 2026Learning from Complexity: Exploring Dynamic Sample Pruning of Spatio-Temporal Trainingcited by 0
  11. 2026Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AIcited by 0