UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation
Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor W. Tsang

TL;DR
UTSGAN introduces a transition-aware generative framework for image-to-image translation, explicitly modeling unseen attribute changes to improve translation consistency across observed and unobserved cases.
Contribution
The paper proposes a novel transition-aware approach with a stochastic transition encoder and transition consistency regularization, enhancing generalization to unseen translations in I2I tasks.
Findings
UTSGAN outperforms existing methods on multiple I2I datasets.
The transition consistency regularization improves translation stability.
The model effectively handles unseen attribute transitions.
Abstract
In the field of Image-to-Image (I2I) translation, ensuring consistency between input images and their translated results is a key requirement for producing high-quality and desirable outputs. Previous I2I methods have relied on result consistency, which enforces consistency between the translated results and the ground truth output, to achieve this goal. However, result consistency is limited in its ability to handle complex and unseen attribute changes in translation tasks. To address this issue, we introduce a transition-aware approach to I2I translation, where the data translation mapping is explicitly parameterized with a transition variable, allowing for the modelling of unobserved translations triggered by unseen transitions. Furthermore, we propose the use of transition consistency, defined on the transition variable, to enable regularization of consistency on unobserved…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Vision and Imaging · Cancer-related molecular mechanisms research
