Self-Supervised Enhancement of Latent Discovery in GANs

Autor:	Sreelatha, Silpa Vadakkeeveetil, Kappiyath, Adarsh, Sumitra, S
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning ComputingMethodologies_PATTERNRECOGNITION Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition General Medicine Machine Learning (cs.LG)
Zdroj:	Proceedings of the AAAI Conference on Artificial Intelligence. 36:7078-7086
ISSN:	2374-3468 2159-5399
Popis:	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. Accepted to the 36th AAAI Conference on Artificial Intelligence (AAAI 2022)
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a2a8c6051ae7c55639e6f2c8317077f https://doi.org/10.1609/aaai.v36i7.20667 Zobrazit plný text záznamu