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Transformation Consistency Regularization – A Semi-supervised Paradigm for Image-to-Image Translation

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12363))

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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.

Codes are made public at https://github.com/aamir-mustafa/Transformation-CR.

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Notes

  1. 1.

    We include all labeled data, without using their labels, alongside the unlabeled data in our transformation consistency regularization term.

  2. 2.

    https://github.com/CSAILVision/places365.

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Acknowledgements

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\(^\circ \) 725253–EyeCode).

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Authors and Affiliations

  1. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

    Aamir Mustafa & Rafał K. Mantiuk

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  1. Aamir Mustafa
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Correspondence to Aamir Mustafa .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Mustafa, A., Mantiuk, R.K. (2020). Transformation Consistency Regularization – A Semi-supervised Paradigm for Image-to-Image Translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_35

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  • DOI: https://doi.org/10.1007/978-3-030-58523-5_35

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