Lune

CVPR2021Top-tier venue

DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

Yuchao Gu, Lijuan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, Ming-Ming Cheng

2021Year
14Top-tier citations

Abstract

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 CI-FAR10/100 and ImageNet demonstrate that DOTS is an effective solution for differentiable NAS. The code is released at https://github.com/guyuchao/DOTS .

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext ccb4ccad-6074-4fcd-b613-782c0797e555

Cited by top-tier papers14

Ask how each one uses it

Builds on21

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines