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Research map / Probabilistic and scientific modeling / Variational and generative sampling

On the Limitations of Multimodal VAEs

ICLR 2022 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Regularizing VAE latent information · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use modality sub-sampling in place of a unimodal VAEMMVAE and MoPoE-VAE · PolyMNIST, Translated-PolyMNIST, and CUB imagesFIDHurt
Remove modality sub-samplingMVAE · PolyMNIST, Translated-PolyMNIST, and CUB imagesFIDHelped
Add modalities (ablation)MMVAE and MoPoE-VAE · PolyMNIST and Translated-PolyMNISTFIDHurt
Add modalities (ablation)MVAE · Translated-PolyMNISTFIDHurt
Use MMVAE or MoPoE-VAE instead of MVAEMultimodal VAEs · PolyMNISTleave-one-out coherenceHelped
Increase beta (ablation)MVAE · PolyMNISTleave-one-out coherenceHelped
Use Translated-PolyMNIST, where shared information cannot be predicted in expectation across modalitiesMMVAE, MoPoE-VAE, and MVAE · Translated-PolyMNISTgenerative coherenceHurt
Conditionally generate images from captions on real-image CUBMMVAE, MoPoE-VAE, and MVAE · CUBgenerative coherenceHurt

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