Image Manipulation with Perceptual Discriminators

By Diana Sungatullina, Egor Zakharov, Dmitry Ulyanov, Victor S. Lempitsky. 2018

24 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Generative Models

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Citations in the corpus

Projected GANs Converge Faster (2021, background): “We also investigate a perceptual discriminator [59], where feature maps are fed into different layers of the same discriminator to predict a single logit.”
Scaling up GANs for Text-to-Image Synthesis (2023, unknown): “We further leverage off-the-shelf pretrained models as a loss function [44, 84, 90].”