Karsten Roth
Resume (pdf)   @confusezius   @karsten-roth   @Confusezius   Karsten Roth

  About me

Currently

May 2021 · IMPRS-IS & ELLIS PhD Student at the University of Tuebingen
  • Co-Supervisors: Prof. Zeynep Akata (University of Tuebingen), Prof. Oriol Vinyals (Google Deepmind, London)
  • Topics: Multimodal Representation Learning | Metric Learning | Generalization in ML | XAI.

Previous

Oct 2020 · Research intern at Amazon AWS, Tuebingen. May 2020 · Research intern at the Vector Institute, Toronto.
  • Supervisor: Prof. Marzyeh Ghassemi.
  • Topics: Few-Shot Learning, Deep Metric Learning, Medical Multilabel Few-Shot Learning.
Sep 2019 · Research intern at MILA, Montreal.

Education

Feb 2021 · Master of Physics from Heidelberg University.

Oct 2014 · Bachelor of Physics from Heidelberg University.


  News

July 2021

Preprint out on out-of-distribution Deep Metric Learning. Link


June 2021

Preprint out on Industrial Anomaly Detection - work done at Amazon AWS. Link


May 2021

Two papers submitted to NeurIPS 2021.


May 2021

One paper accepted to ICML 2021 on Self-Distillation for Deep Metric Learning: Link


May 2021

Started my PhD as part of ELLIS & IMPRS-IS co-supervised by Zeynep Akata (University of Tuebingen) and Oriol Vinyals (Deepmind).


March 2021

Two papers submitted to ICCV 2021.


March 2021

Completed a successful 5 month research internship at AWS Research Lablet Amazon in Tuebingen.


Dec. 2020

Two accepted workshop submissions at NeurIPS 2020 on continual few-shot learning for multilabel medical applications and uniform priors for Meta-learning. Links soon!


March 2021

Started a 5 month research internship at AWS Research Lablet Amazon in Tuebingen supervised by Peter Gehler and Thomas Brox.


Sept. 2020

New preprint on Knowledge Distillation in Deep Metric Learning. Arxiv: Link, Github: Link


July 2020

One paper accepted to TPAMI on Improved Generalization in Deep Metric Learning: Link


July 2020

One paper accepted to ECCV 2020 on Multi-Feature Deep Metric Learning: Link


June 2020

One paper accepted to International Journal for Numerical Methods in Biomedical Engineering on diffuse domain method for needle insertion simulations. Link


June 2020

One paper accepted to ICML 2020 on comparability and generalization in Deep Metric Learning. Arxiv: Link, Github: Link


May 2020

Started a four month research internship at the Vector Institute under supervision of Marzyeh Ghassemi.


Apr. 2020

Selected as participant to attend the MLSS 2020 in Tuebingen (p < 180/1300)


Mar. 2020

One paper submitted to ECCV 2020 on Multi-Feature Deep Metric Learning.


Feb. 2020

One paper accepted to CVPR 2020 on Deep Metric Learning and Reinforcement Learning. Arxiv: Link, Github: Link


Feb. 2020

One paper submitted to ICML 2020 examining driving factors for generalization in Deep Metric Learning.


Feb. 2020

Selected as reviewer for MICCAI 2020.


Jan. 2020

Selected as challenge reviewer for MIDL 2020.


Jan. 2020

One paper accepted to ISBI 2020 on Mask Mining for Liver Lesion Segmentation. Arxiv: Link


Nov. 2019

One paper submitted to CVPR 2020 on Adaptive Deep Metric Learning.


Oct. 2019

One paper submitted to TPAMI on Improved Generalization for Deep Metric Learning.


Oct. 2019

One paper accepted to the NeurIPS 2019 Med-Neurips Workshop Track on error mining for medical semantic segmentation. Arxiv: Link


Sept. 2019

Started a six month research internship at the Montreal Institute for Learning Algorithms (MILA) under supervision of Joseph Paul Cohen and Yoshua Bengio


Sept. 2019

One paper submitted to Numerical Methods in Biomedical Engineering on domain methods for needle insertion simulations.


July 2019

One paper accepted to ICCV 2019 on Deep Metric Learning with Interclass Properties. Arxiv: Link, Github: Link


May 2019

One paper submitted to NeurIPS 2019 on Deep Metric Learning.


April 2019

One paper submitted to MICCAI 2019 on Image Segmentation.


March 2019

One paper submitted to ICCV 2019 on Deep Metric Learning.


  Publications

ICML 2021
Simultaneous Similarity-based Self-Distillatioln for Deep Metric Learning
Karsten Roth, Timo Milbich, Bjoern Ommer, Joseph Paul Cohen*, Marzyeh Ghassemi*
ECCV 2020
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich*, Karsten Roth*, Homanga Bharadhwaj, Samarth Sinha, Yoshua Bengio, Bjoern Ommer, Joseph P. Cohen
TPAMI 2020
Sharing Matters for Generalization in Deep Metric Learning
Timo Milbich*, Karsten Roth*, Biagio Brattoli, Bjoern Ommer
ICML 2020
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth*, Timo Milbich*, Samarth Sinha, Prateek Gupta, Bjoern Ommer, Joseph P. Cohen
CVPR 2020
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Karsten Roth*, Timo Milbich*, Bjoern Ommer
IJBE 2020
Diffuse Domain Method for Needle Insertion Simulation
Katharina I. Jerg, René Phillip Austermühl, Karsten Roth, Jonas Große Sundrup, Guido Kanschat, Jürgen W. Hesser, Lisa Wittmayer
CUREUS 2020
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
Joseph Paul Cohen, Lan Dao, Paul Morrison, Karsten Roth, Yoshua Bengio, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Marzyeh Ghassemi, Haifang Li, Tim Q Duong
ICCV 2019
MIC: Mining Interclass Characteristics for Improved Metric Learning
Karsten Roth*, Biagio Brattoli*, Bjoern Ommer
MICCAI 2017
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, ..., Karsten Roth, ..., Bjoern H. Menze
Phys. A.R. 2014
Efficient preparation and detection of microwave dressed-state qubits and qutrits with trapped ions
Joe Randall, ..., Karsten Roth, Winfried Hensinger