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Found 4 papers, 2 papers with code
Physics-informed Guided Disentanglement in Generative Networks
Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), lowering altogether the translation quality, controllability and variability.
CoMoGAN: continuous model-guided image-to-image translation
CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold.
Model-based occlusion disentanglement for image-to-image translation
Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc.
Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation
Image-to-image translation architectures may have limited effectiveness in some circumstances.
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