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Perceptual Adversarial Networks With a Feature Pyramid for Image Translation.

Liu, Z., Wu, M., Zheng, J. and Yu, H., 2019. Perceptual Adversarial Networks With a Feature Pyramid for Image Translation. IEEE Computer Graphics and Applications, 39 (4), 68 - 77.

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DOI: 10.1109/MCG.2019.2914426

Abstract

This paper investigates the image-to-image translations problems, where the input image is translated into its synthetic form with the original structure and semantics preserved. Widely used methods compute the pixel-wise MSE loss, which are often inadequate for high-frequency content and tend to produce overly smooth results. Concurrent works that leverage recent advances in conditional generative adversarial networks (cGANs) are proposed to enable a universal approach to diverse image translation tasks that traditionally require specific loss functions. Despite the impressive results, most of these approaches are notoriously unstable to train and tend to induce blurs. In this paper, we decompose the image into a set of images by a feature pyramid and elaborate separate loss components for images of specific bandpass. The overall perceptual adversarial loss is able to capture not only the semantic features but also the appearance.

Item Type:Article
ISSN:0272-1716
Group:Faculty of Media & Communication
ID Code:35805
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Jul 2021 12:07
Last Modified:20 Jul 2021 12:07

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