DAVA: Disentangling Adversarial Variational Autoencoder
METADATA ONLY
Loading...
Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
The use of well-disentangled representations offers many advantages for down stream tasks, e.g. an increased sample efficiency, or better interpretability. However, the quality of disentangled interpretations is often highly dependent on the choice of dataset-specific hyperparameters, in particular the regularization strength. To address this issue, we introduce DAVA, a novel training procedure for variational auto-encoders. DAVA completely alleviates the problem of hyperparameter selec tion. We compare DAVA to models with optimal hyperparameters. Without any hyperparameter tuning, DAVA is competitive on a diverse range of commonly used datasets. Underlying DAVA, we discover a necessary condition for unsupervised disentanglement, which we call PIPE. We demonstrate the ability of PIPE to posi tively predict the performance of downstream models in abstract reasoning. We also thoroughly investigate correlations with existing supervised and unsupervised metrics. The code is available at github.com/besterma/dava.
Permanent link
Publication status
published
External links
Editor
Book title
The Eleventh International Conference on Learning Representations (ICLR 2023)
Journal / series
Volume
Pages / Article No.
Publisher
OpenReview
Event
11th International Conference on Learning Representations (ICLR 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03604 - Wattenhofer, Roger / Wattenhofer, Roger
Notes
Poster presentation on May 3, 2023.