Method Details

Details for method 'DCNAS'

name	DCNAS
challenge	pixel-level semantic labeling
details	Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions. However, existing NAS algorithms usually compromise on restricted search space and search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between target and proxy dataset, we propose a Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module to reduce the memory consumption of ample search space.
publication	DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation Xiong Zhang, Hongmin Xu, Hong Mo, Jianchao Tan, Cheng Yang, Wenqi Ren
project page / code
used Cityscapes data	fine annotations, coarse annotations
used external data
runtime	n/a
subsampling	no
submission date	February, 2020
previous submissions