Structural Autoencoders Improve Representations for Generation and Transfer

By Felix Leeb, Yashas Annadani, Stefan Bauer, Bernhard Schölkopf. 2020

13 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

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

In Representation & Self-Supervised Learning

1. Bernhard Schölkopf
2. Francesco Locatello
3. Anirudh Goyal
4. Stefan Bauer
5. Yoshua Bengio
6. Nan Rosemary Ke
7. Yash Sharma
8. Nal Kalchbrenner

Citations in the corpus

• Toward Causal Representation Learning (2021, background): “Leeb et al. [149] propose a structured decoder that embeds an SCM and automatically learns a hierarchy of disentangled factors.”
• Towards Causal Representation Learning (2021, background): “[149] propose a structured decoder that embeds an SCM and automatically learns a hierarchy of disentangled factors.”
• Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style (2021, background): “This is in line with a recently proposed [105] shift towards causal representation learning [40, 76, 85, 87, 106, 107, 112, 123, 127], motivated by the fact that many underlying variables of interest may not be independent but causally related to each other.”