Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation

• URL: http://arxiv.org/abs/2007.07867v1
• Date: Wed, 15 Jul 2020 17:41:35 GMT
• Title: Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation
• Authors: Aamir Mustafa and Rafal K. Mantiuk
• Abstract summary: We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation. We evaluate the efficacy of our algorithm on three different applications: image colorization, denoising and super-resolution. Our method is significantly data efficient, requiring only around 10 - 20% of labeled samples to achieve similar image reconstructions to its fully-supervised counterpart.
• Score: 18.870983535180457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples. Consistency Regularization between model's predictions under different input perturbations, particularly has shown to provide state-of-the art results in a semi-supervised framework. However, most of these method have been limited to classification and segmentation applications. We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation, which remains unexplored by semi-supervised algorithms. The method introduces a diverse set of geometric transformations and enforces the model's predictions for unlabeled data to be invariant to those transformations. We evaluate the efficacy of our algorithm on three different applications: image colorization, denoising and super-resolution. Our method is significantly data efficient, requiring only around 10 - 20% of labeled samples to achieve similar image reconstructions to its fully-supervised counterpart. Furthermore, we show the effectiveness of our method in video processing applications, where knowledge from a few frames can be leveraged to enhance the quality of the rest of the movie.

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