# DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

arXiv: https://arxiv.org/abs/2010.00969 · HTML: https://mlsanity.com/paper/2010.00969/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 23 of 23)

- [β-DARTS: Beta-Decay Regularization for Differentiable Architecture Search](https://mlsanity.com/paper/2203.01665/) (2022)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [Global2Local: Efficient Structure Search for Video Action Segmentation](https://mlsanity.com/paper/2101.00910/) (2021)
- [Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective](https://mlsanity.com/paper/2112.14531/) (2021)
- [Lambda-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells](https://mlsanity.com/paper/2210.07998/) (2022)
- [β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search](https://mlsanity.com/paper/2301.06393/) (2023)
- [On the Privacy Risks of Cell-Based NAS Architectures](https://mlsanity.com/paper/2209.01688/) (2022)
- [Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search](https://mlsanity.com/paper/2304.05405/) (2023)
- [OStr-DARTS: Differentiable Neural Architecture Search based on Operation Strength](https://mlsanity.com/paper/2409.14433/) (2024)
- [When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search](https://mlsanity.com/paper/2204.04918/) (2022)
- [A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search](https://mlsanity.com/paper/2204.03916/) (2022)
- [Efficient Architecture Search via Bi-level Data Pruning](https://mlsanity.com/paper/2312.14200/) (2023)
- [α DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling](https://mlsanity.com/paper/2211.10105/) (2022)
- [DAS: Neural Architecture Search via Distinguishing Activation Score](https://mlsanity.com/paper/2212.12132/) (2022)
- [D-DARTS: Distributed Differentiable Architecture Search](https://mlsanity.com/paper/2108.09306/) (2021)
- [A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick](https://mlsanity.com/paper/2110.15277/) (2021)
- [Exploring Complicated Search Spaces with Interleaving-Free Sampling](https://mlsanity.com/paper/2112.02488/) (2021)
- [WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor](https://mlsanity.com/paper/2203.02086/) (2022)
- [ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks](https://mlsanity.com/paper/2204.05113/) (2022)
- [The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol](https://mlsanity.com/paper/2405.16610/) (2024)
- [FX-DARTS: Designing Topology-unconstrained Architectures with Differentiable Architecture Search and Entropy-based Super-network Shrinking](https://mlsanity.com/paper/2504.20079/) (2025)
- [DNAD: Differentiable Neural Architecture Distillation](https://mlsanity.com/paper/2504.20080/) (2025)
- [MIDAS: Mosaic Input-Specific Differentiable Architecture Search](https://mlsanity.com/paper/2602.17700/) (2026)

## 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.