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Korthals - 2019 - Jointly Trained Variational Autoencoder for Multi-Modal Sensor Fusion.pdf
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🔗 Source: pub.uni-bielefeld.de
📊 Size: 4.13 MB
📄 Pages: 8 pages
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Summary

This work presents a novel multi-modal Variational Autoencoder (M2VAE) approach for sensor fusion, allowing end-to-end training of Bayesian information fusion on raw data for all subsets of a sensor setup. The M2VAE is derived from the complete marginal joint log-likelihood and introduces the concept of in-place fusion, enabling the fusion of latent embeddings of observations with new data. The approach also includes a re-encoding loss to stabilize decoding and facilitate visualization of latent statistics. Experiments demonstrate that the M2VAE finds a coherent latent embedding, allowing a single naive Bayes classifier to perform equally well on all permutations of a bi-modal signal, and outperforms current VAE approaches on bi-modal MNIST and fashion-MNIST datasets.

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Description

This work presents a novel multi-modal Variational Autoencoder (M2VAE) approach for sensor fusion, allowing end-to-end training of Bayesian information fusion...

Technical Information

  • File Format: PDF
  • File Size: 4.13 MB
  • Pages: 8
  • Language: EN
  • Total Downloads: 66
  • Last Updated: 1 month ago

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