Self-Supervised Learning

Large amounts of training data are required for convolutional neural networks so they can learn representations that generalize to novel data. While visual data is easily available, annotation will also in the future be costly, thus motivating self-supervised learning. The challenge is then to have networks learn how to most effectively learn with self-supervision. We have been exploring self-supervision in areas ranging from deep metric and representation learning to visual synthesis and in applications such as behavior analysis in neuroscience.

Talk @ Heidelberg AI, 01/2019

Selected Publications

2021

Roth, Karsten; Milbich, Timo; Ommer, Björn; Cohen, Joseph Paul; Ghassemi, Marzyeh

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

Proceedings of International Conference on Machine Learning (ICML), 2021.

Links | BibTeX

2020

Milbich, Timo; Ghori, Omair; Ommer, Björn

Unsupervised Representation Learning by Discovering Reliable Image Relations Journal Article

In: Pattern Recognition, vol. 102, 2020.

Links | BibTeX

2019

Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis

Esser, Patrick; Haux, Johannes; Ommer, Björn

Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis Conference

Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.

Abstract | Links | BibTeX

Unsupervised Part-Based Disentangling of Object Shape and Appearance

Lorenz, Dominik; Bereska, Leonard; Milbich, Timo; Ommer, Björn

Unsupervised Part-Based Disentangling of Object Shape and Appearance Conference

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions), 2019.

Links | BibTeX

2018

Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning

Büchler, Uta; Brattoli, Biagio; Ommer, Björn

Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning Conference

Proceedings of the European Conference on Computer Vision (ECCV), (UB and BB contributed equally), Munich, Germany, 2018.

Abstract | Links | BibTeX

Sayed, N.; Brattoli, Biagio; Ommer, Björn

Cross and Learn: Cross-Modal Self-Supervision Conference

German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Abstract | Links | BibTeX

Sanakoyeu, A.; Bautista, Miguel; Ommer, Björn

Deep Unsupervised Learning of Visual Similarities Journal Article

In: Pattern Recognition, vol. 78, 2018.

Abstract | Links | BibTeX

2017

Bautista, Miguel; Sanakoyeu, A.; Ommer, Björn

Deep Unsupervised Similarity Learning using Partially Ordered Sets Conference

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

Links | BibTeX

Unsupervised Video Understanding by Reconciliation of Posture Similarities

Milbich, Timo; Bautista, Miguel; Sutter, Ekaterina; Ommer, Björn

Unsupervised Video Understanding by Reconciliation of Posture Similarities Conference

Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.

Links | BibTeX

2016

Bautista, Miguel; Sanakoyeu, A.; Sutter, E.; Ommer, Björn

CliqueCNN: Deep Unsupervised Exemplar Learning Conference

Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), MIT Press, Barcelona, 2016.

Abstract | Links | BibTeX