While machine learning approaches to image restoration offer great promise,
current methods risk training models fixated on performing well only for image
corruption of a particular level of difficulty---such as a certain level of
noise or blur. First, we examine the weakness of conventional "fixated" models
and demonstrate that training general models 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.

本文提出一种基于反馈机制的面向难度级别泛化的深度卷积神经网络图像修复模型自动生成的算法，旨在避免 “固定” 模型对于固定难度级别的训练。实验表明，在四个图像修复任务上，本算法均优于传统训练和课程学习替代方案。