← 研踪 PaperTrailarXiv:2602.17700 · v2
MIDAS: Mosaic Input-Specific Differentiable Architecture Search
首次提交 2026-02-07 07:16
版本更新 2026-10-27 06:16
雷达观测 2026-10-29 00:35
摘要
Differentiable Neural Architecture Search (NAS) provides efficient, gradient-based methods for automatically designing neural networks, yet its adoption remains limited in practice. We present MIDAS, an approach that modernizes DARTS by replacing static architecture parameters with dynamic, input-specific parameters computed via self-attention. To improve robustness, MIDAS (i) localizes the architecture selection by computing it separately for each spatial patch of the activation map, and (ii) introduces a parameter-free, topology-aware search space that models the two-edge node topology during search. We evaluate MIDAS on the DARTS, NAS-Bench-201, and RDARTS search spaces. In DARTS, it reaches 97.42% top-1 on CIFAR-10 and 83.38% on CIFAR-100. In NAS-Bench-201, it discovers optimal or near-optimal architectures. In RDARTS, it achieves the best reported performance among the compared methods on two of four search spaces on CIFAR-10. We further analyze why MIDAS works, showing that patchwise attention improves discrimination among candidate operations and that the resulting input-specific parameter distributions are class-aware.
作者备注：Published in Transactions on Machine Learning Research (TMLR)
期刊信息：Transactions on Machine Learning Research (2026)
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v2 · 2026-10-27 06:16 · 最新观测版本
MIDAS: Mosaic Input-Specific Differentiable Architecture Search
Differentiable Neural Architecture Search (NAS) provides efficient, gradient-based methods for automatically designing neural networks, yet its adoption remains limited in practice. We present MIDAS, an approach that modernizes DARTS by replacing static architecture parameters with dynamic, input-specific parameters computed via self-attention. To improve robustness, MIDAS (i) localizes the architecture selection by computing it separately for each spatial patch of the activation map, and (ii) introduces a parameter-free, topology-aware search space that models the two-edge node topology during search. We evaluate MIDAS on the DARTS, NAS-Bench-201, and RDARTS search spaces. In DARTS, it reaches 97.42% top-1 on CIFAR-10 and 83.38% on CIFAR-100. In NAS-Bench-201, it discovers optimal or near-optimal architectures. In RDARTS, it achieves the best reported performance among the compared methods on two of four search spaces on CIFAR-10. We further analyze why MIDAS works, showing that patchwise attention improves discrimination among candidate operations and that the resulting input-specific parameter distributions are class-aware.
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