Mixture-of-experts VAEs can disregard variation in surjective multimodal data

• URL: http://arxiv.org/abs/2204.05229v1
• Date: Mon, 11 Apr 2022 16:22:51 GMT
• Title: Mixture-of-experts VAEs can disregard variation in surjective multimodal data
• Authors: Jannik Wolff, Tassilo Klein, Moin Nabi, Rahul G. Krishnan, Shinichi Nakajima
• Abstract summary: We consider subjective data, where single datapoints from one modality describe multiple datapoints from another modality. We theoretically and empirically demonstrate that multimodal VAEs with a mixture of experts posterior can struggle to capture variability in such surjective data.
• Score: 23.731871165711635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning systems are often deployed in domains that entail data from multiple modalities, for example, phenotypic and genotypic characteristics describe patients in healthcare. Previous works have developed multimodal variational autoencoders (VAEs) that generate several modalities. We consider subjective data, where single datapoints from one modality (such as class labels) describe multiple datapoints from another modality (such as images). We theoretically and empirically demonstrate that multimodal VAEs with a mixture of experts posterior can struggle to capture variability in such surjective data.

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