Self-supervised Enhancement of Latent Discovery in GANs
This work addresses the issue of enhancing interpretability in GANs for researchers and practitioners, but it is incremental as it builds upon existing unsupervised disentanglement techniques.
The paper tackled the problem of less disentangled latent semantics in unsupervised methods for discovering interpretable directions in GANs, and the result was that the proposed Scale Ranking Estimator (SRE) significantly improved disentanglement in various datasets, as shown through qualitative and quantitative evaluation.
Several methods for discovering interpretable directions in the latent space of pre-trained GANs have been proposed. Latent semantics discovered by unsupervised methods are relatively less disentangled than supervised methods since they do not use pre-trained attribute classifiers. We propose Scale Ranking Estimator (SRE), which is trained using self-supervision. SRE enhances the disentanglement in directions obtained by existing unsupervised disentanglement techniques. These directions are updated to preserve the ordering of variation within each direction in latent space. Qualitative and quantitative evaluation of the discovered directions demonstrates that our proposed method significantly improves disentanglement in various datasets. We also show that the learned SRE can be used to perform Attribute-based image retrieval task without further training.