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Local distance preserving autoencoders using continuous kNN graphs
Nutan Chen · Patrick van der Smagt · Botond Cseke

Fri Jul 22 01:45 PM -- 03:00 PM (PDT) @

In this paper, we introduce several auto-encoder models that preserve local distances in the latent space. We use a local distance preserving loss that is based on the continuous k-nearest neighbour graph which is known to capture topological features at all scales simultaneously. To improve training performance, we formulate learning as a constraint optimisation problem with local distance preservation as the main objective and reconstruction accuracy as a constraint. We generalise this approach to hierarchical variational auto-encoders thus learning generative models with geometrically consistent latent and data spaces. Our method provides state-of-the-art performance across several standard datasets and evaluation metrics.

Author Information

Nutan Chen (Machine Learning Research Lab, Volkswagen group)
Patrick van der Smagt (Volkswagen Group)
Botond Cseke (Volkswagen Group)

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