Hellinger Multimodal Variational Autoencoders
📰 ArXiv cs.AI
Hellinger Multimodal Variational Autoencoders revise multimodal inference using probabilistic opinion pooling
Action Steps
- Revisit multimodal inference using probabilistic opinion pooling
- Apply Hellinger distance to aggregate unimodal inference distributions
- Approximate the joint posterior using the proposed method
- Evaluate the performance of the Hellinger Multimodal VAEs on multimodal datasets
Who Needs to Know This
ML researchers and engineers working on multimodal generative models can benefit from this approach to improve inference and learning
Key Insight
💡 Probabilistic opinion pooling can be used to improve multimodal inference in VAEs
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Key Takeaways
Hellinger Multimodal Variational Autoencoders revise multimodal inference using probabilistic opinion pooling
Full Article
Title: Hellinger Multimodal Variational Autoencoders
Abstract:
arXiv:2601.06572v2 Announce Type: replace-cross Abstract: Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities. Predominant methods aggregate unimodal inference distributions using either a product of experts (PoE), a mixture of experts (MoE), or their combinations to approximate the joint posterior. In this work, we revisit multimodal inference through the lens of probabilistic opinion pooling, an optimization-based approa
Abstract:
arXiv:2601.06572v2 Announce Type: replace-cross Abstract: Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities. Predominant methods aggregate unimodal inference distributions using either a product of experts (PoE), a mixture of experts (MoE), or their combinations to approximate the joint posterior. In this work, we revisit multimodal inference through the lens of probabilistic opinion pooling, an optimization-based approa
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