mlsanityData to 7 Oct 2026

DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

arXiv 2010.00969 ↗First posted 2 Oct 2020cs.CVcited by 23

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Abstract (arXiv)

Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search and derives the cell topology from the operation weights. However, the operation weights can not indicate the importance of cell topology and result in poor topology rating correctness. To tackle this, we propose to Decouple the Operation and Topology Search (DOTS), which decouples the topology representation from operation weights and makes an explicit topology search. DOTS is achieved by introducing a topology search space that contains combinations of candidate edges. The proposed search space directly reflects the search objective and can be easily extended to support a flexible number of edges in the searched cell. Existing gradient-based NAS methods can be incorporated into DOTS for further improvement by the topology search. Considering that some operations (e.g., Skip-Connection) can affect the topology, we propose a group operation search scheme to preserve topology-related operations for a better topology search. The experiments on CIFAR10/100 and ImageNet demonstrate that DOTS is an effective solution for differentiable NAS.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2020.

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Citing papers per year: 2021: 6; 2022: 9; 2023: 3; 2024: 2; 2025: 2; 2026: 1.

23 papers.

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  1. 2022β-DARTS: Beta-Decay Regularization for Differentiable Architecture Searchcited by 63
  2. 2021DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentationcited by 47
  3. 2021Global2Local: Efficient Structure Search for Video Action Segmentationcited by 42
  4. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  5. 2022Lambda-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cellscited by 15
  6. 2023β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Searchcited by 7
  7. 2022On the Privacy Risks of Cell-Based NAS Architecturescited by 5
  8. 2023Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Searchcited by 5
  9. 2024OStr-DARTS: Differentiable Neural Architecture Search based on Operation Strengthcited by 4
  10. 2022When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Searchcited by 3
  11. 2022A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Searchcited by 2
  12. 2023Efficient Architecture Search via Bi-level Data Pruningcited by 2
11 more
  1. 2022α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modelingcited by 1
  2. 2022DAS: Neural Architecture Search via Distinguishing Activation Scorecited by 1
  3. 2021D-DARTS: Distributed Differentiable Architecture Searchcited by 0
  4. 2021A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trickcited by 0
  5. 2021Exploring Complicated Search Spaces with Interleaving-Free Samplingcited by 0
  6. 2022WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictorcited by 0
  7. 2022ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networkscited by 0
  8. 2024The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocolcited by 0
  9. 2025FX-DARTS: Designing Topology-unconstrained Architectures with Differentiable Architecture Search and Entropy-based Super-network Shrinkingcited by 0
  10. 2025DNAD: Differentiable Neural Architecture Distillationcited by 0
  11. 2026MIDAS: Mosaic Input-Specific Differentiable Architecture Searchcited by 0