Deep convolutional 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.
翻译:深相神经网络在超分辨率域中展示了令人印象深刻的结果。 一系列研究侧重于通过使用对受限资源不友好的更深层来改善峰值信号噪声比( PSNR ) 。 追求恢复能力与模型简单性之间的权衡仍然是非三重的。 最近的贡献正试图手工最大限度地实现这一平衡, 而我们的工作则通过神经结构的搜索自动达到同样的目标。 具体地说, 我们用多目标的方法处理超分辨率。 我们还提议在微观和宏观两级采用弹性搜索策略, 其基础是从进化计算和强化学习中获利的混合控制器。 定量实验有助于我们得出一个结论,即我们生成的模型在个人FLOPS方面控制了大多数最先进的方法。