Contrastive learning

Contrastive learning is a machine learning technique used to learn a representation of the input data that maximizes the difference between samples of different classes and minimizes the difference between samples of the same class. The learned representation (or features) will then be used to solve a classification problem. In this tutorial, we show this effective learning technique from head to toe through an image classification example. As you can see, contrastive learning plays a role of feature extractor which helps subsequent classification training to achieve higher accuracy.

The code: https://staff.sharcnet.ca/guanw/2023/...
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This webinar was presented by Weiguang Guan (SHARCNET) on May 17th, 2023, as a part of a series of weekly Compute Ontario Colloquia. The webinar was hosted by SHARCNET. The colloquia cover different advanced research computing (ARC) and high performance computing (HPC) topics, are approximately 45 minutes in length, and are delivered by experts in the relevant fields. Further details can be found on this web page: https://www.computeontario.ca/trainin... . Recordings, slides, and other materials can be found here: https://helpwiki.sharcnet.ca/wiki/Onl...

SHARCNET is a consortium of 19 Canadian academic institutions who share a network of high performance computers (http://www.sharcnet.ca). SHARCNET is a part of Compute Ontario (http://computeontario.ca/) and Digital Research Alliance of Canada (https://alliancecan.ca).