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Physics-informed Guided Disentanglement in Generative Networks

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Abstract

Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), thus lowering the translation quality and variability. In this paper, we present a comprehensive method for disentangling physics-based traits in the translation, guiding the learning process with neural or physical models. For the latter, we integrate adversarial estimation and genetic algorithms to correctly achieve disentanglement. The results show our approach dramatically increase performances in many challenging scenarios for image translation.

Dates and versions

hal-03498130 , version 1 (20-12-2021)

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Fabio Pizzati, Pietro Cerri, Raoul de Charette. Physics-informed Guided Disentanglement in Generative Networks. 2022. ⟨hal-03498130⟩

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