This paper proposes a novel method combining contrastive learning with conditional variational autoencoders and mutual information constraints to extract style features from unlabeled data, demonstrating effectiveness on simple datasets like MNIST while facing challenges with natural image datasets due to augmentation limitations and qualitative evaluation.
Unsupervised Learning, Self-Supervised Learning, Contrastive Learning, Representation Learning, Feature Engineering, Multimodal Data
Suguru Yasutomi, Toshihisa Tanaka
Tokyo University of Agriculture and Technology
Generated by grok-3
Background Problem
Extracting fine-grained style features from unlabeled data is a critical challenge in unsupervised learning, as traditional methods like variational autoencoders (VAEs) often mix styles with other features, and conditional VAEs (CVAEs) require labeled data to isolate styles. This paper addresses the gap by proposing a method to extract style features without labels, solving the problem of separating style from content in datasets where explicit class information is unavailable.
Method
The proposed method integrates a contrastive learning (CL) model with a conditional variational autoencoder (CVAE) to extract style features from unlabeled data. The core idea is to use CL to learn style-independent features (z_content) through self-supervised learning with data augmentation, which are then used as conditions for the CVAE to extract style-specific features (z_style). The method employs a two-step training process: first, a CL model (e.g., MoCo v2) is pretrained to isolate content features robust to augmentation perturbations; second, the CVAE is trained with a loss function combining reconstruction loss, Kullback-Leibler divergence (KLD), and a mutual information (MI) constraint estimated via Mutual Information Neural Estimation (MINE) to ensure independence between z_content and z_style. This MI constraint prevents the CVAE from ignoring the condition by encouraging statistical independence between content and style features, implemented through a min-max optimization problem akin to adversarial learning.
Experiment
The experiments were conducted on four datasets: MNIST and a custom Google Fonts dataset for simpler validation, and Imagenette and DAISO-100 for real-world natural image testing. The setup used ResNet-18/50 as backbones for CL and CVAE models, with data augmentation including random perspective, cropping, blurring, and brightness/contrast changes. Various CL methods (MoCo v2, SimCLR, SimSiam, VICReg) were tested alongside a supervised variant for comparison. Results showed that the method successfully extracted style features like slant and thickness in MNIST, and bounding box characteristics in Google Fonts, though it failed to capture font faces as styles due to unsuitable augmentation. For natural image datasets, style transfer and neighbor analysis indicated partial success (e.g., brightness and background styles in DAISO-100), but generated image quality was poor, and Imagenette results were less interpretable due to sparse feature spaces. The qualitative nature of evaluations limits conclusive evidence of superiority, and results did not fully meet expectations for complex datasets, highlighting dependency on augmentation strategies and the need for quantitative metrics.
Further Thoughts
The dependency of the proposed method on data augmentation strategies for CL raises an interesting connection to domain adaptation research, where domain-specific features are often isolated through adversarial or discrepancy-based methods. Future work could explore integrating domain adaptation techniques to enhance style isolation, especially for datasets with diverse style variations like natural images. Additionally, the qualitative limitation of the evaluation could be addressed by drawing inspiration from disentanglement studies, where metrics like mutual information gap or separability scores are used to quantify feature independence. Another insightful direction is the potential application to time-series data, as suggested by the authors, particularly in domains like audio processing where style (e.g., speaker identity) and content (e.g., spoken words) separation is crucial. Combining this method with temporal contrastive learning frameworks could yield robust style extraction for sequential data, opening avenues for cross-domain applications in signal processing and beyond.