On-Demand Learning for Deep Image Restoration
release_7y7mzrmttzetnndj44ag5u6j5u
by
Ruohan Gao, Kristen Grauman
2016
Abstract
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.
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