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Fast Neural Architecture Search for Lightweight Dense Prediction Networks

arXiv 2203.01994 ↗First posted 3 Mar 2022cs.CVcited by 0

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Abstract (arXiv)

We present LDP, a lightweight dense prediction neural architecture search (NAS) framework. Starting from a pre-defined generic backbone, LDP applies the novel Assisted Tabu Search for efficient architecture exploration. LDP is fast and suitable for various dense estimation problems, unlike previous NAS methods that are either computational demanding or deployed only for a single subtask. The performance of LPD is evaluated on monocular depth estimation, semantic segmentation, and image super-resolution tasks on diverse datasets, including NYU-Depth-v2, KITTI, Cityscapes, COCO-stuff, DIV2K, Set5, Set14, BSD100, Urban100. Experiments show that the proposed framework yields consistent improvements on all tested dense prediction tasks, while being 5%-315% more compact in terms of the number of model parameters than prior arts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 91 of its 115 references that are in the map. First posted on arXiv 3 Mar 2022.

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References · 91

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38 more
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