Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders With Mutual Information Constraints

Extracting fine-grained features such as styles from unlabeled data is crucial for data analysis. Unsupervised methods such as variational autoencoders (VAEs) can extract styles that are usually mixed with other features. Conditional VAEs (CVAEs) can isolate styles using class labels; however, there...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 37; číslo 5; s. 3001 - 3014
Hlavní autori:	Yasutomi, Suguru, Tanaka, Toshihisa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.05.2025
Predmet:	Autoencoders contrastive learning Data augmentation Data mining Data models Feature extraction Mutual information Perturbation methods Style extraction Training Unsupervised learning variational autoencoders Vectors
ISSN:	1041-4347, 1558-2191
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