Antonin Vobecky presents Contrastive learning for visual representations - Part 2

On 2020-12-10 11:00:00 at
Online reading group on "Contrastive learning for visual representations" -
Part 2

Contrastive learning has recently attracted a lot of attention as a means to
learn visual representation in an unsupervised way. This is the second part,
out of two, presenting some of the relevant approaches. We will present [1]
introduces a simpler approach for contrastive unsupervised learning, and
continue the discussion with [2] which discusses the benefits of models trained
supervision even in setups of additional training with labeling.

[1] A Simple Framework for Contrastive Learning of Visual Representations
, Chen et al., arxiv 2020
[2] Big Self-Supervised Models are Strong Semi-Supervised Learners, Chen et
arxiv 2020

Video conference link:

Instructions for participants: The reading group studies the literature in the
field of pattern recognition and computer vision. At each meeting one or more
papers are prepared for presentation by a single person, the presenter. The
meetings are open to anyone, disregarding their background. It is assumed that
everyone attending the reading group has, at least briefly, read the paper –
not necessarily understanding everything. Attendants should preferably send
questions about the unclear parts to the speaker at least one day in advance.
During the presentation we aim to have a fruitful discussion, a critical
analysis of the paper, as well as brainstorming for creative extensions.

See the page of reading groups
Responsible person: Petr Pošík