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LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
[article]
2021
arXiv
pre-print
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable controllable image generation and support a wide range of semantic editing operations, such as zoom or rotation. The discovery of such directions is often done in a supervised or semi-supervised manner and requires manual annotations which limits their use in practice. In comparison, unsupervised discovery allows
arXiv:2104.00820v2
fatcat:v7gcu5mjureyfa5o2uvvfjk6pm