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http://arxiv.org/abs/2002.05709
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* L. Jing and Y. Tian, Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey, CVPR2019 <br>
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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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... | ... | @@ -53,13 +54,16 @@ Code: https://github.com/TobyPDE/FRRN |
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Paper: https://arxiv.org/abs/1908.07919<br>
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Code: https://paperswithcode.com/paper/deep-high-resolution-representation-learning-2
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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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Venue: **INM-1 Seminar room**, building 15.9, room 4001b
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* Deep Speech 2: End-to-End Speech Recognition in English and Mandarin, Amodei et al., 2015<br>
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https://arxiv.org/abs/1512.02595
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https://arxiv.org/abs/1512.02595<br>
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Not the most recent paper about speech recognition, but a breakthrough for language modelling, thus worth reading. It discusses some topics, which are relevant for timeseries analysis, and also makes reference to good use of HPC. Following this topic, we could continue with latest papers on that at next Journal Club.
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* and, if needed, as background paper about deep recurrent networks:<br>
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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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