[Foundation Models][LLMs][CLIP]
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
We critically study how recently arising approaches to utilize LLM descriptions to extend Vision-Language model performance (s.a. CLIP) truly affect their behaviour. Our experiments reveal limited semantic benefits, allowing us to introduce a simple alternative solely relying on structured input noise.
Karsten Roth*, Jae Myung Kim*, A. Sophia Koepke, Oriol Vinyals, Cordelia Schmid, Zeynep Akata
ICCV, 2023
arXiv | code

[Representation Learning][Out-of-Distribution]
Disentanglement of Correlated Factors via Hausdorff Factorized Support
We tackle the problem of disentangled representation learning without the unrealistic assumption of independent factors of variation, i.e. we allow for correlation in the training data. We achieve this through a novel, Hausdorff-distance based objective by factorizing the support instead of the distribution over latents.
Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent*, Diane Bouchacourt*
ICLR, 2023
arXiv

[Foundation Models][Continual Learning]
Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning
This work showcases that foundation models suffer from continual adaptation, which can be fixed surprisingly easily by retaining a weight-interpolated momentum version, without the need to re-integrate the momentum network back into training.
Zafir Stojanovski*, Karsten Roth*, Zeynep Akata
[Best Paper] INTERPOLATE @ NeurIPS 2022
arXiv

[Contrastive Learning][Uncertainty]
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning
In this work, we propose a novel probabilistic approach to Deep Metric Learning by describing embeddings and proxies as (non-isotropic) distributions, and the problem of metric learning as that of distribution matching.
Michael Kirchhof*, Karsten Roth*, Zeynep Akata, Enkelejda Kasneci
ECCV, 2022
arXiv

[Contrastive Learning][Large Language Models]
Integrating Language Guidance into Vision-based Deep Metric Learning
We showcase the benefits of re-aligning visual similarity spaces using language semantics, without the need of additional expert supervision and with significant improvements in generalization performance.
Karsten Roth, Oriol Vinyals, Zeynep Akata
[Oral] CVPR, 2022
arXiv | code

[Representation Learning][Out-of-Distribution]
Non-isotropy Regularization for Proxy-based Deep Metric Learning
This work showcases the benefits of resolving local structures in proxy-based Deep Metric Learning without sample-to-sample relations. Doing so retains incredibly fast convergence speeds while ensuring strong generalization performance.
Karsten Roth, Oriol Vinyals, Zeynep Akata
CVPR, 2022
arXiv | code

[Anomaly Detection]
Towards Total Recall in Industrial Anomaly Detection
We develop PatchCore - an anomaly detection method for visual product inspection, which is scalable, fast, extremely accurate, interpretable and usable without expert knowledge. Using Coreset Memories, PatchCore has retained the state-of-the-art for more than a year now.
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schoelkopf, Thomas Brox, Peter Gehler
CVPR, 2022
arXiv | code

[Contrastive Learning][Fairness]
Is fairness only metric deep? evaluating and addressing subgroup gaps in deep metric learning
This work proposes finDML, the fairness in non-balanced DML benchmark to characterize representation fairness. We find bias in DML representations to propagate to common downstream classification tasks, even when training data in the downstream task is re-balanced, and propose a regularizer to tackle this.
Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi
ICLR, 2022
arXiv | proceedings | bibtex

[Cell Tracking][Cell Segmentation]
Temporal control of the integrated stress response by a stochastic molecular "switch"
Five year interdisciplinary project studying and evaluating integrated cellular stress response.
Philipp Klein, Stefan M.Kallenberger, Hanna Roth, Karsten Roth, Thi Bach Nga Ly-Hartig, Vera Magg, Janez Aleš, Soheil Rastgou Talemi, Yu Qiang, Steffen Wolf, Olga Oleksiuk, Roma Kurilov, Barbara Di Ventura, Ralf Bartenschlager, Roland Eils, Karl Rohr, Fred A. Hamprecht, Thomas Höfer, Oliver T. Fackler, Georg Stoecklin, Alessia Ruggieri
Science Advances, 2022
Science

[Contrastive Learning]
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
A seminal project that highlights significant performance saturation in Deep Metric Learning research and the reasons, as well as an initial study into structural drivers of generalization in Deep Metric Learning.
Karsten Roth*, Timo Milbich*, Samarth Sinha, Prateek Gupta, Bjoern Ommer, Joseph Paul Cohen
ICML, 2020
arXiv | proceedings | bibtex | code

[Contrastive Learning][Reinforcement Learning]
Pads: Policy-adapted sampling for visual similarity learning
Using Reinforcement Learning, PADS introduces a tuple mining heuristic that presents the network the right tuples to learn from at the right time.
Karsten Roth*, Timo Milbich*, Bjoern Ommer
CVPR, 2020
arXiv | proceedings | bibtex | code