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In this work, we propose a contrastive-learning-based approach for discovering semantic directions in the latent space of pretrained GANs in a self-supervised manner.

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach …

Invertibility of normalizing flows can be exploited to define latent space perturbations that yield helpful data augmentations for classifier training, particularly in low-data regimes.