Timezone: »

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)

More from the Same Authors