Search Results for author: Karsten Roth

Found 21 papers, 16 papers with code

Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning

no code implementations6 Nov 2022 Zafir Stojanovski, Karsten Roth, Zeynep Akata

Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts.

Continual Learning

Disentanglement of Correlated Factors via Hausdorff Factorized Support

1 code implementation13 Oct 2022 Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt

We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over $+60\%$ in relative improvement over existing disentanglement methods.


A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning

1 code implementation8 Jul 2022 Michael Kirchhof, Karsten Roth, Zeynep Akata, Enkelejda Kasneci

We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere that can reflect image-intrinsic uncertainties.

Metric Learning

Improving the Fairness of Chest X-ray Classifiers

1 code implementation23 Mar 2022 Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Robert Pfohl, Marzyeh Ghassemi

We also find that methods which achieve group fairness do so by worsening performance for all groups.


Integrating Language Guidance into Vision-based Deep Metric Learning

1 code implementation CVPR 2022 Karsten Roth, Oriol Vinyals, Zeynep Akata

This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes, impacting the generalizability of the learned metric space.

Ranked #5 on Metric Learning on CARS196 (using extra training data)

Metric Learning

Non-isotropy Regularization for Proxy-based Deep Metric Learning

1 code implementation CVPR 2022 Karsten Roth, Oriol Vinyals, Zeynep Akata

Deep Metric Learning (DML) aims to learn representation spaces on which semantic relations can simply be expressed through predefined distance metrics.

Ranked #8 on Metric Learning on CUB-200-2011 (using extra training data)

Metric Learning

Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning

2 code implementations NeurIPS 2021 Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer

Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.

Metric Learning

S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning

1 code implementation17 Sep 2020 Karsten Roth, Timo Milbich, Björn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi

Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives.

Ranked #7 on Metric Learning on CARS196 (using extra training data)

Knowledge Distillation Metric Learning +1

Uniform Priors for Data-Efficient Transfer

no code implementations30 Jun 2020 Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Hugo Larochelle, Animesh Garg

Deep Neural Networks have shown great promise on a variety of downstream applications; but their ability to adapt and generalize to new data and tasks remains a challenge.

Domain Adaptation Meta-Learning +1

COVID-19 Image Data Collection: Prospective Predictions Are the Future

6 code implementations22 Jun 2020 Joseph Paul Cohen, Paul Morrison, Lan Dao, Karsten Roth, Tim Q Duong, Marzyeh Ghassemi

This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19.


Sharing Matters for Generalization in Deep Metric Learning

no code implementations12 Apr 2020 Timo Milbich, Karsten Roth, Biagio Brattoli, Björn Ommer

The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes.

Metric Learning

PADS: Policy-Adapted Sampling for Visual Similarity Learning

1 code implementation CVPR 2020 Karsten Roth, Timo Milbich, Björn Ommer

Learning visual similarity requires to learn relations, typically between triplets of images.

Ranked #14 on Metric Learning on CUB-200-2011 (using extra training data)

Metric Learning

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

8 code implementations ICML 2020 Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Björn Ommer, Joseph Paul Cohen

Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year.

Metric Learning

MIC: Mining Interclass Characteristics for Improved Metric Learning

2 code implementations ICCV 2019 Karsten Roth, Biagio Brattoli, Björn Ommer

In contrast, we propose to explicitly learn the latent characteristics that are shared by and go across object classes.

Ranked #16 on Metric Learning on CUB-200-2011 (using extra training data)

Image Retrieval Metric Learning +1

Mask Mining for Improved Liver Lesion Segmentation

no code implementations14 Aug 2019 Karsten Roth, Jürgen Hesser, Tomasz Konopczyński

We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models.

Lesion Segmentation Tumor Segmentation

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +2

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