| ... | ... | @@ -13,25 +13,6 @@ If you’re interested in more details about the Journal Club, please subscribe |
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## Next Meeting
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### Monday 18 May 10-11:30am Variational Autoencoders
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Venue: if possible f2f JSC meetingroom 2, building 16.3; room 315 <br>
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alternatively online: https://webconf.fz-juelich.de/b/wen-mym-pj7
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* Original VAE paper<br>
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Auto-Encoding Variational Bayes<br>
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Diederik P Kingma, Max Welling, ICLR 2014<br>
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https://arxiv.org/abs/1312.6114 <br>
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https://openreview.net/forum?id=33X9fd2-9FyZd <br>
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14 pages, incl appendix
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* Recent VAE review / tutorial<br>
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An Introduction to Variational Autoencoders<br>
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Diederik P. Kingma, Max Welling (2019) Foundations and Trends in Machine Learning. 12. 307-392. 10.1561/2200000056. <br>
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https://arxiv.org/abs/1906.02691<br>
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86 pages <br>
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## Schedule for upcoming Meetings
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### 15 June 2020 - Generative Adversarial Networks (and VAE)
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Venue: JSC meetingroom 2, building 16.3; room 315 <br>
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| ... | ... | @@ -49,7 +30,10 @@ Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing, ICLR 2017<br> |
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https://arxiv.org/abs/1706.00550 <br>
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https://openreview.net/forum?id=rylSzl-R- <br>
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16 pages, incl appendix
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## Schedule for upcoming Meetings
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### 20 July 2020 - Summer break
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### 17 August 2020 - Attention Networks
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| ... | ... | @@ -75,16 +59,35 @@ alternatively online: https://webconf.fz-juelich.de/b/wen-mym-pj7 |
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* A Simple Framework for Contrastive Learning of Visual Representations <br>
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T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, 2020<br>
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http://arxiv.org/abs/2002.05709
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http://arxiv.org/abs/2002.05709<br>
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15 pages, incl appendix
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* Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey <br>
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L. Jing and Y. Tian, CVPR2019<br>
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http://arxiv.org/abs/1902.06162
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http://arxiv.org/abs/1902.06162<br>
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21 pages
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The first paper presents a state-of-the-art approach for self-supervised learning of strong visual features based on contrastive learning. Random data augmentation is applied to images from the ImageNet dataset and a model is trained to match augmented and original images. The second paper revisits several self-supervised training techniques for visual representation learning and offers a nice overview over different approaches.
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## Past Meetings
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### Monday 18 May 10-11:30am Variational Autoencoders
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Venue: if possible f2f JSC meetingroom 2, building 16.3; room 315 <br>
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alternatively online: https://webconf.fz-juelich.de/b/wen-mym-pj7
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* Original VAE paper<br>
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Auto-Encoding Variational Bayes<br>
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Diederik P Kingma, Max Welling, ICLR 2014<br>
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https://arxiv.org/abs/1312.6114 <br>
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https://openreview.net/forum?id=33X9fd2-9FyZd <br>
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14 pages, incl appendix
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* Recent VAE review / tutorial<br>
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An Introduction to Variational Autoencoders<br>
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Diederik P. Kingma, Max Welling (2019) Foundations and Trends in Machine Learning. 12. 307-392. 10.1561/2200000056. <br>
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https://arxiv.org/abs/1906.02691<br>
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86 pages <br>
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### Monday 20 April - Learning discrete representations from data
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* A. van den Oord, O. Vinyals, K. Kavukcuoglu, Neural Discrete Representation Learning, NeurIPS 2017<br>
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| ... | ... | @@ -155,4 +158,4 @@ A training schedule using filter pruning and orthogonal reinitialization |
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---
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last change: 24.4.2020 sw |
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\ No newline at end of file |
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last change: 18.5.2020 sw |
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\ No newline at end of file |
