Deep Metric and Representation Learning

@conference{Milbich2021,

title = {Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning},

author = {Timo Milbich and Karsten Roth and Samarth Sinha and Ludwig Schmidt and Marzyeh Ghassemi and Björn Ommer},

url = {https://github.com/CompVis/Characterizing_Generalization_in_DML

https://arxiv.org/abs/2107.09562},

year  = {2021},

date = {2021-12-10},

urldate = {2021-12-10},

booktitle = {Neural Information Processing Systems (NeurIPS)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{7051,

title = {S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning},

author = {Karsten Roth and Timo Milbich and Björn Ommer and Joseph Paul Cohen and Marzyeh Ghassemi},

url = {https://github.com/MLforHealth/S2SD

https://arxiv.org/abs/2009.08348},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6934,

title = {DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning},

author = {Timo Milbich and Karsten Roth and Homanga Bharadhwaj and Samarth Sinha and Yoshua Bengio and Björn Ommer and Joseph Paul Cohen},

url = {https://arxiv.org/abs/2004.13458},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {IEEE European Conference on Computer Vision (ECCV)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6390,

title = {Revisiting Training Strategies and Generalization Performance in Deep Metric Learning},

author = {Karsten Roth and Timo Milbich and Samarth Sinha and Prateek Gupta and Björn Ommer and Joseph Paul Cohen},

url = {https://arxiv.org/abs/2002.08473},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {International Conference on Machine Learning (ICML)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6386,

title = {PADS: Policy-Adapted Sampling for Visual Similarity Learning},

author = {Timo Milbich and Karsten Roth and Björn Ommer},

url = {https://github.com/Confusezius/CVPR2020_PADS

https://arxiv.org/abs/2003.11113},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

volume = {1},

chapter = {1},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6299,

title = {Divide and Conquer the Embedding Space for Metric Learning},

author = {A. Sanakoyeu and V. Tschernezki and Uta Büchler and Björn Ommer},

url = {https://github.com/CompVis/metric-learning-divide-and-conquer

https://arxiv.org/abs/1906.05990},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6321,

title = {MIC: Mining Interclass Characteristics for Improved Metric Learning},

author = {Biagio Brattoli and Karsten Roth and Björn Ommer},

url = {https://github.com/Confusezius/ICCV2019_MIC

https://arxiv.org/abs/1909.11574},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Proceedings of the Intl. Conf. on Computer Vision (ICCV)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{buechler:ECCV:2018,

title = {Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning},

author = {Uta Büchler and Biagio Brattoli and Björn Ommer},

url = {https://arxiv.org/abs/1807.11293

https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/1855931744/buechler_eccv18_poster.pdf},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},

publisher = {(UB and BB contributed equally)},

address = {Munich, Germany},

abstract = {Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations. As surrogate task, we jointly address ordering of visual data in the spatial and temporal domain. The permutations of training samples, which are at the core of self-supervision by ordering, have so far been sampled randomly from a fixed preselected set. Based on deep reinforcement learning we propose a sampling policy that adapts to the state of the network, which is being trained. Therefore, new permutations are sampled according to their expected utility for updating the convolutional feature representation. Experimental evaluation on unsupervised and transfer learning tasks demonstrates competitive performance on standard benchmarks for image and video classification and nearest neighbor retrieval.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6200,

title = {Deep Unsupervised Similarity Learning using Partially Ordered Sets},

author = {Miguel Bautista and A. Sanakoyeu and Björn Ommer},

url = {https://github.com/asanakoy/deep_unsupervised_posets

https://arxiv.org/abs/1704.02268},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{6187,

title = {Unsupervised Video Understanding by Reconciliation of Posture Similarities},

author = {Timo Milbich and Miguel Bautista and Ekaterina Sutter and Björn Ommer},

url = {https://hci.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17

https://arxiv.org/abs/1708.01191},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{arXiv:1608.08792,

title = {CliqueCNN: Deep Unsupervised Exemplar Learning},

author = {Miguel Bautista and A. Sanakoyeu and E. Sutter and Björn Ommer},

url = {https://github.com/asanakoy/cliquecnn

https://arxiv.org/abs/1608.08792},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS)},

publisher = {MIT Press},

address = {Barcelona},

abstract = {Exemplar learning is a powerful paradigm for discovering visual similarities in 

an unsupervised manner. In this context, however, the recent breakthrough in 

deep learning could not yet unfold its full potential. With only a single positive 

sample, a great imbalance between one positive and many negatives, and unreliable 

relationships between most samples, training of Convolutional Neural networks is 

impaired. Given weak estimates of local distance we propose a single optimization 

problem to extract batches of samples with mutually consistent relations. Conflict- 

ing relations are distributed over different batches and similar samples are grouped 

into compact cliques. Learning exemplar similarities is framed as a sequence of 

clique categorization tasks. The CNN then consolidates transitivity relations within 

and between cliques and learns a single representation for all samples without 

the need for labels. The proposed unsupervised approach has shown competitive 

performance on detailed posture analysis and object classification.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}