Title: Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search,
Abstract: Deep convolution neural networks demonstrate impressive results in the
super-resolution domain. A series of studies concentrate on improving peak
signal noise ratio (PSNR) by using much deeper layers, which are not friendly
to constrained resources. Pursuing a trade-off between the restoration capacity
and the simplicity of models is still non-trivial. Recent contributions are
struggling to manually maximize this balance, while our work achieves the same
goal automatically with neural architecture search. Specifically, we handle
super-resolution with a multi-objective approach. We also propose an elastic
search tactic at both micro and macro level, based on a hybrid controller that
profits from evolutionary computation and reinforcement learning. Quantitative
experiments help us to draw a conclusion that our generated models dominate
most of the state-of-the-art methods with respect to the individual FLOPS.