Selbstüberwachtes Lernen (German Wikipedia)

Analysis of information sources in references of the Wikipedia article "Selbstüberwachtes Lernen" in German language version.

Last modified:

Ref.Un. Ref.Website
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3,285th place
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aaai.org (Global: low place; German: low place)

  • Dahun Kim, Donghyeon Cho, In So Kweon: Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles. In: Proceedings of the AAAI Conference on Artificial Intelligence. Band 33, Nr. 01, 17. Juli 2019, ISSN 2374-3468, S. 8545–8552, doi:10.1609/aaai.v33i01.33018545 (aaai.org [abgerufen am 3. November 2020]).

arxiv.org (Global: 49th place; German: 152nd place)

  • Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch: Data-Efficient Image Recognition with Contrastive Predictive Coding. 1. Juli 2020, arxiv:1905.09272 [abs].

cv-foundation.org (Global: low place; German: low place)

  • Carl Doersch, Abhinav Gupta, Alexei A. Efros: Unsupervised Visual Representation Learning by Context Prediction. 2015, S. 1422–1430 (cv-foundation.org [abgerufen am 3. November 2020]).

doi.org (Global: 2nd place; German: 3rd place)

  • Xin Zheng, Yong Wang, Guoyou Wang, Jianguo Liu: Fast and robust segmentation of white blood cell images by self-supervised learning. In: Micron. Band 107, 1. April 2018, ISSN 0968-4328, S. 55–71, doi:10.1016/j.micron.2018.01.010 (sciencedirect.com [abgerufen am 3. November 2020]).
  • Mehdi Noroozi, Paolo Favaro: Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. In: Computer Vision – ECCV 2016. Band 9910. Springer International Publishing, Cham 2016, ISBN 978-3-319-46465-7, S. 69–84, doi:10.1007/978-3-319-46466-4_5.
  • Longlong Jing, Yingli Tian: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020, ISSN 0162-8828, S. 1–1, doi:10.1109/TPAMI.2020.2992393 (ieee.org [abgerufen am 3. November 2020]).
  • Dahun Kim, Donghyeon Cho, In So Kweon: Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles. In: Proceedings of the AAAI Conference on Artificial Intelligence. Band 33, Nr. 01, 17. Juli 2019, ISSN 2374-3468, S. 8545–8552, doi:10.1609/aaai.v33i01.33018545 (aaai.org [abgerufen am 3. November 2020]).
  • J. Scholtz, B. Antonishek, J. Young: Operator interventions in autonomous off-road driving: effects of terrain. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583). IEEE, ISBN 0-7803-8567-5, doi:10.1109/icsmc.2004.1400756.
  • M Ye, E Johns, A Handa, L Zhang, P Pratt: Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery. In: 10th Hamlyn Symposium on Medical Robotics 2017. The Hamlyn Centre, Faculty of Engineering, Imperial College London, 2017, ISBN 978-0-9563776-8-5, doi:10.31256/hsmr2017.14.
  • Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter: Dense Depth Estimation in Monocular Endoscopy With Self-Supervised Learning Methods. In: IEEE Transactions on Medical Imaging. Band 39, Nr. 5, Mai 2020, ISSN 0278-0062, S. 1438–1447, doi:10.1109/tmi.2019.2950936.

facebook.com (Global: 78th place; German: 110th place)

ai.facebook.com

googleblog.com (Global: 3,285th place; German: 1,811th place)

ai.googleblog.com

ieee.org (Global: 912th place; German: 894th place)

ieeexplore.ieee.org

  • Longlong Jing, Yingli Tian: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020, ISSN 0162-8828, S. 1–1, doi:10.1109/TPAMI.2020.2992393 (ieee.org [abgerufen am 3. November 2020]).

medium.com (Global: 651st place; German: 793rd place)

sciencedirect.com (Global: 137th place; German: 321st place)

thecvf.com (Global: low place; German: low place)

openaccess.thecvf.com

  • Carl Doersch, Andrew Zisserman: Multi-Task Self-Supervised Visual Learning. 2017, S. 2051–2060 (thecvf.com [abgerufen am 3. November 2020]).
  • Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer: S4L: Self-Supervised Semi-Supervised Learning. 2019, S. 1476–1485 (thecvf.com [abgerufen am 3. November 2020]).
  • Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Perez, Matthieu Cord: Boosting Few-Shot Visual Learning With Self-Supervision. 2019, S. 8059–8068 (thecvf.com [abgerufen am 3. November 2020]).

towardsdatascience.com (Global: low place; German: low place)

zdb-katalog.de (Global: 107th place; German: 5th place)

  • Xin Zheng, Yong Wang, Guoyou Wang, Jianguo Liu: Fast and robust segmentation of white blood cell images by self-supervised learning. In: Micron. Band 107, 1. April 2018, ISSN 0968-4328, S. 55–71, doi:10.1016/j.micron.2018.01.010 (sciencedirect.com [abgerufen am 3. November 2020]).
  • Longlong Jing, Yingli Tian: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020, ISSN 0162-8828, S. 1–1, doi:10.1109/TPAMI.2020.2992393 (ieee.org [abgerufen am 3. November 2020]).
  • Dahun Kim, Donghyeon Cho, In So Kweon: Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles. In: Proceedings of the AAAI Conference on Artificial Intelligence. Band 33, Nr. 01, 17. Juli 2019, ISSN 2374-3468, S. 8545–8552, doi:10.1609/aaai.v33i01.33018545 (aaai.org [abgerufen am 3. November 2020]).
  • Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Austin Reiter: Dense Depth Estimation in Monocular Endoscopy With Self-Supervised Learning Methods. In: IEEE Transactions on Medical Imaging. Band 39, Nr. 5, Mai 2020, ISSN 0278-0062, S. 1438–1447, doi:10.1109/tmi.2019.2950936.