736 citations · 938 across the 14 of their papers we have counts for
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A Note on Data Biases in Generative Models
Patrick Esser, Robin Rombach, Björn Ommer
It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter a…
Taming Transformers for High-Resolution Image Synthesis
Patrick Esser, Robin Rombach, Björn Ommer
Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain n…
Unsupervised Part Discovery by Unsupervised Disentanglement
Sandro Braun, Patrick Esser, Björn Ommer
We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part loc…
Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs
Robin Rombach, Patrick Esser, Björn Ommer
To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly…
Network-to-Network Translation with Conditional Invertible Neural Networks
Robin Rombach, Patrick Esser, Björn Ommer
Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been…
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Patrick Esser, Robin Rombach, Björn Ommer
Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall perf…