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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| Vydáno v: | IEEE transactions on knowledge and data engineering Ročník 37; číslo 5; s. 3001 - 3014 |
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| Jazyk: | angličtina |
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01.05.2025
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| ISSN: | 1041-4347, 1558-2191 |
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| Abstract | 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 are no established methods to extract only styles using unlabeled data. In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data. The proposed model consists of a contrastive learning (CL) part that extracts style-independent features and a CVAE part that extracts style features. The CL model learns representations independent of data augmentation, which can be viewed as a perturbation in styles, in a self-supervised manner. Considering the style-independent features from the pretrained CL model as a condition, the CVAE learns to extract only styles. Additionally, we introduce a constraint based on mutual information between the CL and VAE features to prevent the CVAE from ignoring the condition. Experiments conducted using two simple datasets, MNIST and an original dataset based on Google Fonts, demonstrate that the proposed method can efficiently extract style features. Further experiments using real-world natural image datasets were also conducted to illustrate the method's extendability. |
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| AbstractList | 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 are no established methods to extract only styles using unlabeled data. In this paper, we propose a CVAE-based method that extracts style features using only unlabeled data. The proposed model consists of a contrastive learning (CL) part that extracts style-independent features and a CVAE part that extracts style features. The CL model learns representations independent of data augmentation, which can be viewed as a perturbation in styles, in a self-supervised manner. Considering the style-independent features from the pretrained CL model as a condition, the CVAE learns to extract only styles. Additionally, we introduce a constraint based on mutual information between the CL and VAE features to prevent the CVAE from ignoring the condition. Experiments conducted using two simple datasets, MNIST and an original dataset based on Google Fonts, demonstrate that the proposed method can efficiently extract style features. Further experiments using real-world natural image datasets were also conducted to illustrate the method's extendability. |
| Author | Tanaka, Toshihisa Yasutomi, Suguru |
| Author_xml | – sequence: 1 givenname: Suguru orcidid: 0009-0006-0626-982X surname: Yasutomi fullname: Yasutomi, Suguru email: yasutomi@sip.tuat.ac.jp organization: Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Koganei-Shi, Japan – sequence: 2 givenname: Toshihisa orcidid: 0000-0002-5056-9508 surname: Tanaka fullname: Tanaka, Toshihisa organization: Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Koganei-Shi, Japan |
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| SubjectTerms | Autoencoders contrastive learning Data augmentation Data mining Data models Feature extraction Mutual information Perturbation methods Style extraction Training Unsupervised learning variational autoencoders Vectors |
| Title | Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders With Mutual Information Constraints |
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