  In this paper we propose a novel network adaption method called
Differentiable Network Adaption (DNA), which can adapt an existing network to a
specific computation budget by adjusting the width and depth in a
differentiable manner. The gradient-based optimization allows DNA to achieve an
automatic optimization of width and depth rather than previous heuristic
methods that heavily rely on human priors. Moreover, we propose a new elastic
search space that can flexibly condense or expand during the optimization
process, allowing the network optimization of width and depth in a bi-direction
manner. By DNA, we successfully achieve network architecture optimization by
condensing and expanding in both width and depth dimensions. Extensive
experiments on ImageNet demonstrate that DNA can adapt the existing network to
meet different targeted computation requirements with better performance than
previous methods. What's more, DNA can further improve the performance of
high-accuracy networks obtained by state-of-the-art neural architecture search
methods such as EfficientNet and MobileNet-v3.

Computer Vision and Pattern Recognition;Machine Learning