* A Simple Framework for Contrastive Learning of Visual Representations <br>
* A Simple Framework for Contrastive Learning of Visual Representations <br>
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, 2020<br>
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, 2020<br>
http://arxiv.org/abs/2002.05709
http://arxiv.org/abs/2002.05709<br>
15 pages, incl appendix
* Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey <br>
* Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey <br>
L. Jing and Y. Tian, CVPR2019<br>
L. Jing and Y. Tian, CVPR2019<br>
http://arxiv.org/abs/1902.06162
http://arxiv.org/abs/1902.06162<br>
21 pages
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.
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.
## Past Meetings
## Past Meetings
### Monday 18 May 10-11:30am Variational Autoencoders
Venue: if possible f2f JSC meetingroom 2, building 16.3; room 315 <br>
