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Transformation Consistency Regularization - A Semi Supervised Paradigm for Image-to-Image Translation
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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Publication
Aamir Mustafa and Rafał K. Mantiuk. Transformation Consistency Regularization - A Semi Supervised Paradigm for Image-to-Image Translation. European Conference on Computer Vision (ECCV), 2020
Contact
Please contact Aamir Mustafa or Rafał K. Mantiuk with any questions regarding the method.
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Acknowledgement
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement N◦ 725253–EyeCode).