### Monday 21 September 10-11:30am - Self-Supervised Visual Representation Learning
Venue: tba
* T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, A Simple Framework for Contrastive Learning of Visual Representations <br>
http://arxiv.org/abs/2002.05709
* L. Jing and Y. Tian, Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey, CVPR2019 <br>
http://arxiv.org/abs/1902.06162
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.
