1.4k citations · 4.2k across the 41 of their papers we have counts for
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Recent Advances in Autoencoder-Based Representation Learning
Michael Tschannen, Olivier Bachem, Mario Lucic
Learning useful representations with little or no supervision is a key challenge in artificial intelligence. We provide an in-depth review of recent advances in representation lear…
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered…
Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter +2
Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labeled data. In this work we exploit two popular unsupervis…
On Self Modulation for Generative Adversarial Networks
Ting Chen, Mario Lucic, Neil Houlsby +1
Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance acro…
A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lucic, Xiaohua Zhai +2
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully appli…
Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic +2
Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison. Commonly used evaluation methods, suc…