<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Self-Supervised Learning | Academic</title><link>https://gabriel-bertocco.netlify.app/tag/self-supervised-learning/</link><atom:link href="https://gabriel-bertocco.netlify.app/tag/self-supervised-learning/index.xml" rel="self" type="application/rss+xml"/><description>Self-Supervised Learning</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Mon, 30 May 2022 15:43:10 +0000</lastBuildDate><image><url>https://gabriel-bertocco.netlify.app/media/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_3.png</url><title>Self-Supervised Learning</title><link>https://gabriel-bertocco.netlify.app/tag/self-supervised-learning/</link></image><item><title>Mining Persons, Objects, and Places from Heterogeneous Source Domains</title><link>https://gabriel-bertocco.netlify.app/project/mining-persons-objects-and-places-from-heterogeneous-source-domains/</link><pubDate>Mon, 30 May 2022 15:43:10 +0000</pubDate><guid>https://gabriel-bertocco.netlify.app/project/mining-persons-objects-and-places-from-heterogeneous-source-domains/</guid><description>&lt;p>This is my current project that I have focused on. The ultimate goal is to design large-scale self-supervised learning solutions to filter and group persons, objects and places in fully-unsupervised manner avoiding biases. We envision to design solutions that can also&lt;br>
be extended for applications in further Artificial Intelligence research fields. It is part of the &lt;a href="https://www.ic.unicamp.br/~dejavu/" target="_blank" rel="noopener">Déjàvu Project&lt;/a>.&lt;/p></description></item></channel></rss>