Learning disentangled representations with the Wasserstein Autoencoder

7 October 2020

Papers citing "Learning disentangled representations with the Wasserstein Autoencoder"

3 / 3 papers shown

Title
Disentanglement Learning via Topology Nikita Balabin Daria Voronkova I. Trofimov Evgeny Burnaev S. Barannikov DRL 60 2 0 24 Aug 2023
Gromov-Wasserstein Autoencoders Nao Nakagawa Ren Togo Takahiro Ogawa Miki Haseyama GAN DRL 26 11 0 15 Sep 2022
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data Kamil Faber Roberto Corizzo B. Sniezynski Michael Baron Nathalie Japkowicz AI4TS 31 22 0 18 Jan 2022