Fast Neural Architecture Search for Lightweight Dense Prediction Networks

Abstract

We present LDP, a lightweight dense prediction neural architecture search (NAS) framework. Starting from a predefined 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, Ur-ban100. 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.