4 papers
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…
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…
Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis
Patrick Esser, Johannes Haux, Björn Ommer
Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main c…