2020

DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

Zhang, Xiong, Xu, Hongmin, Mo, Hong et al.

Understand

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.

Built on

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