Face Identity Disentanglement via Latent Space Mapping
ID-disentanglement-Pytorch Pytorch implementation of the paper Face Identity Disentanglement via Latent Space Mapping for both training and evaluation, with StyleGAN 2. Changes from original paper instead of using a Discriminator loss for the mapper. We have used several other losses such as: LPIPS Loss (The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, Zhang el al, 2018) MSE Loss Different ID Loss Different landmark detector The reason for those changes resides in the fact that the training procedure with […]
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