Image manipulation with perceptual discriminators

Diana Sungatullina, Egor Zakharov, Dmitry Ulyanov, Victor Lempitsky

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)


    Systems that perform image manipulation using deep convolutional networks have achieved remarkable realism. Perceptual losses and losses based on adversarial discriminators are the two main classes of learning objectives behind these advances. In this work, we show how these two ideas can be combined in a principled and non-additive manner for unaligned image translation tasks. This is accomplished through a special architecture of the discriminator network inside generative adversarial learning framework. The new architecture, that we call a perceptual discriminator, embeds the convolutional parts of a pre-trained deep classification network inside the discriminator network. The resulting architecture can be trained on unaligned image datasets, while benefiting from the robustness and efficiency of perceptual losses. We demonstrate the merits of the new architecture in a series of qualitative and quantitative comparisons with baseline approaches and state-of-the-art frameworks for unaligned image translation.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
    EditorsMartial Hebert, Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu
    PublisherSpringer Verlag
    Number of pages16
    ISBN (Print)9783030012304
    Publication statusPublished - 2018
    Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
    Duration: 8 Sep 201814 Sep 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11210 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference15th European Conference on Computer Vision, ECCV 2018


    • Generative adversarial networks
    • Image editing
    • Image translation
    • Perceptual loss


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