358 citations · 620 across the 4 of their papers we have counts for
4 papers
Semantic Bottleneck Scene Generation
Samaneh Azadi, Michael Tschannen, Eric Tzeng +3
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottl…
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov +14
Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets. Yet, the absence of a unified evaluation for general…
High-Fidelity Image Generation With Fewer Labels
Mario Lucic, Michael Tschannen, Marvin Ritter +3
Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimen…
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…