Topic: "Contrastive learning"

Speaker: Weiguang Guan, SHARCNET

Video link

Recording

 

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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 Compute Ontario Colloquia are weekly Zoom presentations on Advanced Research Computing, High Performance Computing, Research Data Management, and Research Software topics, delivered by staff from three Compute Ontario consortia (CAC, SciNet, SHARCNET) and guest speakers. The series began January 2023 and superseded similar series previously delivered by individual consortia (e.g. General Interest Seminars by SHARCNET or User Group Meeting TechTalks by SciNet). The colloquia are one hour long and include time for questions. No registration is required. Presentations are usually recorded and uploaded to the hosting consortium video channel (colloquia hosted by SHARCNET go to our youtube channel).