1 citations · 1 across the 3 of their papers we have counts for
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Physics-informed Guided Disentanglement in Generative Networks
Fabio Pizzati, Pietro Cerri, Raoul de Charette
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 altoge…
CoMoGAN: continuous model-guided image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normaliza…
Model-based occlusion disentanglement for image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised mode…
Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation
Fabio Pizzati, Raoul de Charette, Michela Zaccaria +1
Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits…