... | ... | @@ -11,9 +11,24 @@ When: **every 3rd Monday of the month, 10pm** |
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If you’re interested in more details about the Journal Club, please subscribe for the mailinglist at https://lists.fz-juelich.de/mailman/listinfo/julain_journal_club
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## Monday 18 May 10-11:30am - Self-Supervised Visual Representation Learning
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## Next Meeting
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Venue: JSC meetingroom 2, building 16.3, room 315
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### Monday 18 May 10-11:30am Variational Autoencoders
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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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* 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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## Schedule for upcoming Meetings
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### Monday 21 September 10-11:30am - Self-Supervised Visual Representation Learning
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Venue: tba
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* T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, A Simple Framework for Contrastive Learning of Visual Representations <br>
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http://arxiv.org/abs/2002.05709
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... | ... | @@ -23,8 +38,9 @@ http://arxiv.org/abs/1902.06162 |
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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 20 April 10-11:30am - Learning discrete representations from data
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### Monday 20 April 10-11:30am - Learning discrete representations from data
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Venue: JSC meetingroom 2, building 16.3, room 315
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... | ... | @@ -36,7 +52,7 @@ https://arxiv.org/abs/1906.00446 |
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Those two papers are about learning discrete representations from data by taking inspiration from vector quantization. Learning discrete representations using neural networks is challenging and can helpful for tasks such as compression, planning, reasoning, and can be potentially more interpretable than continuous ones. In the two papers use those learned discrete representations to build autoregressive generative models on image, sound, and video. The second paper (Generating Diverse High-Fidelity Images with VQ-VAE-2) is basically a sequel of the first (Neural Discrete Representation Learning) where they scale the models to bigger datasets and images (up to 1024x1024 resolution).
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## Monday 30 March 10-11:30am - Full-Resolution Residual Networks for Semantic Segmentation
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### Monday 30 March 10-11:30am - Full-Resolution Residual Networks for Semantic Segmentation
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**Replacement date for canceled meeting at Monday 16 March 10-11:30am**
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... | ... | @@ -56,7 +72,7 @@ Code: https://paperswithcode.com/paper/deep-high-resolution-representation-learn |
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Most common architectures for semantic segmentation consisting of an encoder and decoder part (e.g. U-Net) heavily reduce the spatial dimension of input images and may loose important details or fail to localize precisely. The proposed papers present full-resolution networks, which try to preserve high-resolution features throughout the network and improve localization accuracy.
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## Monday 17 February 10-11:30am - Speech Recognition
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### Monday 17 February 10-11:30am - Speech Recognition
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Venue: **INM-1 Seminar room**, building 15.9, room 4001b
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... | ... | @@ -68,7 +84,7 @@ Speech Recognition with Deep Recurrent Neural Networks<br> |
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Alex Graves, Abdel-rahman Mohamed, Geoffrey Hinton, 2013<br>
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https://arxiv.org/abs/1303.5778
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## Monday 20 January 10-11:30am
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### Monday 20 January 10-11:30am
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Venue: **INM-1 Seminar room**, building 15.9, room 4001b
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... | ... | @@ -78,7 +94,7 @@ https://publications.waset.org/3524/pdf |
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https://arxiv.org/pdf/1909.12778v3.pdf
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## Monday 16 December 10-11am
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### Monday 16 December 10-11am
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Venue: **JSC, Rotunda**, building 16.4, room 301
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