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Taming Transformers for High-Resolution Image Synthesis (a.k.a #VQGAN) Patrick Esser*, Robin Rombach*, Björn Ommer IWR, Heidelberg University CVPR 2021 (ORAL) TL;DR: We introduce the convolutional VQGAN to combine both the efficiency of convolutional approaches with the expressive power of transformers, and to combine adversarial with likelihood training in a perceptually meaningful way. The VQGAN
A Variational U-Net for Conditional Appearance and Shape Generation Patrick Esser*, Ekaterina Sutter*, Björn Ommer IWR, Heidelberg University CVPR 2018 Abstract Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of th
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