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Found 4 papers, 3 papers with code
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data
Since our synthesizer only considers the generic properties of natural images, the single model pretrained on our dataset can be consistently transferred to various target datasets, and even outperforms the previous methods pretrained with the natural images in terms of Fr'echet inception distance.
GridMix: Strong regularization through local context mapping
Recently developed regularization techniques improve the networks generalization by only considering the global context.
Rethinking the Truly Unsupervised Image-to-Image Translation
To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains.
Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously
Also, we show that our model can achieve the competitive performance with the state-of-the-art attribute editing technique in terms of attribute editing quality.
