From One-Trick Ponies to All-Rounders: On-Demand Learning for Image Restoration.

While machine learning approaches to image restoration offer great promise, current methods risk training ponies that perform well only for image corruption of a particular level of difficulty---such as a certain level of noise or blur. First, we expose the weakness of todayu0027s one-trick pony and demonstrate that training general models equipped to handle arbitrary levels of corruption is indeed non-trivial. Then, we propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks. The main idea is to exploit a feedback mechanism to self-generate training instances where they are needed most, thereby learning models that can generalize across difficulty levels. On four restoration tasks---image inpainting, pixel interpolation, image deblurring, and image denoising---and three diverse datasets, our approach consistently outperforms both the status quo training procedure and curriculum learning alternatives.