Visual Computing

University of Konstanz
Proceedings of the Workshop on Visualization for AI Explainability (VISxAI)

Towards an Interpretable Latent Space

T. Spinner, J. Körner, J. Görtler, O. Deussen

Material

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Abstract

We present an intuitive comparison of Auto-Encoders (AE) with Variational Auto-Encoders (VAE) by visualizing their latent activations. In order to do this, we trained an AE and the corresponding VAE on the MNIST dataset. To give a feeling for the latent compression, we visualize the latent activations of the AE/VAE by displaying the 4 latent variables in a parallel coordinate system. We provide an introduction to the architectures of AEs/VAEs and draw a comparison between the two models.

BibTeX

@inproceedings{Spinner2018TowardsInterpretableLatent,
  author     = {T. Spinner and J. Körner and J. Görtler and O. Deussen},
  booktitle  = {Proceedings of the Workshop on Visualization for AI Explainability (VISxAI)},
  title      = {Towards an Interpretable Latent Space},
  url        = {https://spinthil.github.io/towards-an-interpretable-latent-space/},
  year       = {2018},
}