Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance

📰 ArXiv cs.AI

Learn to address class imbalance in semi-supervised learning using a multimodal deep generative model, crucial for unbiased modeling of imbalanced data

advanced Published 9 May 2026

Action Steps

Implement a multimodal deep generative model to handle class-imbalanced data
Use pseudo-labels for unlabeled data and account for potential bias
Configure the model to incorporate multiple input modalities
Train the model using a combination of labeled and unlabeled data
Evaluate the model's performance on a held-out test set to assess its ability to handle class imbalance

Who Needs to Know This

Data scientists and machine learning engineers working on semi-supervised learning projects with class-imbalanced data will benefit from this approach, as it helps to reduce bias towards majority classes

Key Insight

💡 Multimodal deep generative models can effectively address class imbalance in semi-supervised learning by incorporating multiple input modalities and reducing bias towards majority classes

Key Takeaways

Learn to address class imbalance in semi-supervised learning using a multimodal deep generative model, crucial for unbiased modeling of imbalanced data

Full Article

Title: Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance

Abstract:
arXiv:2605.06289v1 Announce Type: cross Abstract: When modeling class-imbalanced data, it is crucial to address the imbalance, as models trained on such data tend to be biased towards the majority classes. This problem is amplified under partial supervision, where pseudo-labels for unlabeled data are predicted based on imbalanced labeled data, propagating the bias. While recent semi-supervised models address class imbalance, they typically assume single-modal input data. However, with the growin

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