Improving Deep Generative Models on Many-To-One Image-to-Image Translation

arXiv:2402.12531v1 Announce Type: cross
Abstract: Deep generative models have been applied to multiple applications in image- to-image translation. Generative Adversarial Networks and Diffusion Models have presented impressive results, setting new state-of-the-art results on these tasks. Most methods have symmetric setups across the different domains in a dataset. These methods assume that all domains have either multiple modalities or only one modality. However, there are many datasets that have a many-to-one relationship between two domains. In this work, we first introduce …

abstract adversarial applications art arxiv cs.cv cs.lg dataset deep generative models diffusion diffusion models domains generative generative adversarial networks generative models image image-to-image image-to-image translation multiple networks state tasks translation type