@article {xGiro-i-Nieto20, title = {Deep Self-Supervised Learning for All}, year = {2020}, abstract = {

Deep neural networks have achieved outstanding results in various applications such as vision, language, audio, speech, or reinforcement learning. These powerful function approximators typically require large amounts of data to be trained, which poses a challenge in the usual case where little labeled data is available. During the last year, multiple solutions have been proposed to leverage this problem, based on the concept of self-supervised learning, which can be understood as a specific case of unsupervised learning. This talk will cover its basic principles and provide examples in the field of multimedia.



Deep Self-supervised Learning for All - Xavier Giro - X-Europe 2020 from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://deep-self-supervised-learning.carrd.co/}, author = {Xavier Gir{\'o}-i-Nieto} }