Learning Disentangled Representations with Wasserstein Auto-Encoders

By Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin. 2018

29 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Francesco Locatello
2. Bernhard Schölkopf
3. Michael Tschannen
4. Stefan Bauer
5. Mario Lucic
6. Olivier Bachem
7. Yoshua Bengio
8. Gunnar Rätsch

Citations in the corpus

• Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018, background): “Several variations of VAEs were proposed with the motivation that they lead to better disentanglement (Higgins et al., 2017a; Burgess et al., 2017; Kim & Mnih, 2018; Chen et al., 2018; Kumar et al., 2017; Rubenstein et al., 2018).”
• Disentangling Factors of Variation Using Few Labels (2019, background): “Current state-of-the-art unsupervised disentanglement approaches enrich the Variational Autoencoder (VAE) [33] objective with different unsupervised regularizers that aim to encourage disentangled representations [20, 5, 31, 6, 36, 54, 44, 53].”
• Disentangling Factors of Variations Using Few Labels (2020, background): “Current state-of-the-art unsupervised disentanglement approaches enrich the Variational Autoencoder (VAE) (Kingma & Welling, 2014) objective with different unsupervised regularizers that aim to encourage disentangled representations (Higgins et al., 2017a; Burgess et al., 2018; Kim & Mnih, 2018; Che”