Evaluating Sparsity for Improving Disentanglement in Wasserstein Autoencoders: Several sparsity-inducing penalties are implemented within the Wasserstein Autoencoders framework to quantify their effect on learning disentangled representations.
Disentangling via Transformation-Robust Representations: Invariance against groups of transformations on particular latent space dimensions is imposed, allowing to incorporate prior knowledge on a weakly-supervised level.
Contact
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Prof. Dr. Christoph Lippert Professor for Digital Health & Machine Learning Room: G-2.1.xx Tel.: +49-(0)331 5509-4850 E-Mail: christoph.lippert(at)hpi.de
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