On-Demand Learning for Deep Image Restoration

Ruohan Gao; Kristen Grauman

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arXiv · 1612.01380

On-Demand Learning for Deep Image Restoration

Ruohan Gao·Kristen Grauman

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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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BibTeX RIS

Ruohan Gao, Kristen Grauman. 2016-12-05. On-Demand Learning for Deep Image Restoration. https://arxiv.org/abs/1612.01380

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