Papers › CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search

CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search

18 Jul 2020ECCV 2020 8arXiv:2007.09380archive 2025-07-28

Xin Chen, Yawen Duan, Zewei Chen, Hang Xu, Zihao Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li

Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They also lack efficient mechanisms to reuse knowledge when confronting multiple tasks. These challenges preclude their applicability, and motivate our proposal of CATCH, a novel Context-bAsed meTa reinforcement learning (RL) algorithm for transferrable arChitecture searcH. The combination of meta-learning and RL allows CATCH to efficiently adapt to new tasks while being agnostic to search spaces. CATCH utilizes a probabilistic encoder to encode task properties into latent context variables, which then guide CATCH's controller to quickly "catch" top-performing networks. The contexts also assist a network evaluator in filtering inferior candidates and speed up learning. Extensive experiments demonstrate CATCH's universality and search efficiency over many other widely-recognized algorithms. It is also capable of handling cross-domain architecture search as competitive networks on ImageNet, COCO, and Cityscapes are identified. This is the first work to our knowledge that proposes an efficient transferrable NAS solution while maintaining robustness across various settings.

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Tasks

Meta Reinforcement Learning Meta-Learning Neural Architecture Search Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Neural Architecture Search	NAS-Bench-201, CIFAR-10	CATCH-meta	Accuracy (Val)	91.33	#37 of 37	Archive leaderboard	report
Neural Architecture Search	NAS-Bench-201, CIFAR-100	CATCH-meta	Accuracy (Val)	72.57	#39 of 40	Archive leaderboard	report
Neural Architecture Search	NAS-Bench-201, ImageNet-16-120	CATCH-meta	Accuracy (Val)	46.07	#46 of 49	Archive leaderboard	report
Neural Architecture Search	NAS-Bench-201, ImageNet-16-120	CATCH-meta	Search time (s)	18000	#46 of 49	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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