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The Contextual Loss for Image Transformation with Non-aligned Data

  • Roey Mechrez
  • Itamar Talmi
  • Lihi Zelnik-Manor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)

Abstract

Feed-forward CNNs trained for image transformation problems rely on loss functions that measure the similarity between the generated image and a target image. Most of the common loss functions assume that these images are spatially aligned and compare pixels at corresponding locations. However, for many tasks, aligned training pairs of images will not be available. We present an alternative loss function that does not require alignment, thus providing an effective and simple solution for a new space of problems. Our loss is based on both context and semantics – it compares regions with similar semantic meaning, while considering the context of the entire image. Hence, for example, when transferring the style of one face to another, it will translate eyes-to-eyes and mouth-to-mouth. Our code can be found at https://www.github.com/roimehrez/contextualLoss.

Notes

Acknowledgements

This research was supported by the Israel Science Foundation under Grant 1089/16 and by the Ollendorf foundation.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Technion - Israel Institute of TechnologyHaifaIsrael

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