Search Results for author: Peter Gehler

Found 20 papers, 8 papers with code

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

no code implementations ICLR 2022 Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf

Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions.

Trajectory Prediction

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain

1 code implementation ICLR 2022 Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world.

Representation Learning Test

The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents

no code implementations ICLR 2022 Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer

By training 240 representations and over 10, 000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents.

Reinforcement Learning (RL) Representation Learning

If your data distribution shifts, use self-learning

1 code implementation27 Apr 2021 Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge

We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts.

 Ranked #1 on Unsupervised Domain Adaptation on ImageNet-C (using extra training data)

Robust classification Self-Learning +1

Towards causal generative scene models via competition of experts

no code implementations27 Apr 2020 Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf

Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world.

Inductive Bias

Rehabilitating the ColorChecker Dataset for Illuminant Estimation

no code implementations30 May 2018 Ghalia Hemrit, Graham D. Finlayson, Arjan Gijsenij, Peter Gehler, Simone Bianco, Brian Funt, Mark Drew, Lilong Shi

In a previous work, it was shown that there is a curious problem with the benchmark ColorChecker dataset for illuminant estimation.

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

1 code implementation ECCV 2018 Sergey Prokudin, Peter Gehler, Sebastian Nowozin

However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.

Pose Estimation Probabilistic Deep Learning +1

3D Object Class Detection in the Wild

no code implementations17 Mar 2015 Bojan Pepik, Michael Stark, Peter Gehler, Tobias Ritschel, Bernt Schiele

Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations.

object-detection Object Detection +1

Multi-View Priors for Learning Detectors from Sparse Viewpoint Data

no code implementations20 Dec 2013 Bojan Pepik, Michael Stark, Peter Gehler, Bernt Schiele

While the majority of today's object class models provide only 2D bounding boxes, far richer output hypotheses are desirable including viewpoint, fine-grained category, and 3D geometry estimate.

Object Localization Transfer Learning +1

Poselet Conditioned Pictorial Structures

no code implementations CVPR 2013 Leonid Pishchulin, Mykhaylo Andriluka, Peter Gehler, Bernt Schiele

In this paper we consider the challenging problem of articulated human pose estimation in still images.

Pose Estimation

Occlusion Patterns for Object Class Detection

no code implementations CVPR 2013 Bojan Pepikj, Michael Stark, Peter Gehler, Bernt Schiele

Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion remains a major challenge, and a principled solution is yet to be found.

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