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Sminario di Informatica: “Beyond Supervised Learning”

Venerdì 28 settembre 2018, ore 14:30, Aula 1BC50 - Paolo Favaro

ARGOMENTI: Seminars

Venerdì 28 settembre 2018 alle ore 14:30 in Aula 1BC50, Paolo Favaro (University of Bern) terrà un seminario dal titolo “Beyond Supervised Learning”.

Abstract
Supervised learning is a powerful technique to build models that can associate data to given targets. Today this is a successful method that is widely adopted in the industry. However, supervised learning comes with limitations: It relies on costly, time-consuming and error-prone manual labeling of a large set of examples. Moreover, animals do not seem to learn about objects through a teacher during their lifetime. It seems possible that much of the learning occurs in an unsupervised manner. I will illustrate two general ideas that show a path towards learning without annotation: self-supervised learning and unsupervised disentangling of factors of variations. Self-supervised learning has emerged as a successful method to learn useful features without manual labeling. I will also discuss a second approach to unsupervised learning, which aims at disentangling the main factors of variation of data. The idea behind this approach is to automatically cluster variability in the data that can be represented by the same attribute. For example, if data consists of faces, possible factors of variation are the gender, the hair style, the presence of glasses, beard, hats and scarfs, the pose, the expression, the skin color, and so on. I will present techniques that allow identifying such factors in an unsupervised manner or with weak labels.

Short Bio
Paolo Favaro is full professor at the University of Bern, Switzerland, where he heads the Computer Vision Group. He received the Laurea degree (B.Sc.+M.Sc.) from Università di Padova, Italy in 1999, and the M.Sc. and Ph.D. degree in electrical engineering from Washington University in St. Louis in 2002 and 2003 respectively. He was a postdoctoral researcher in the computer science department of the University of California, Los Angeles and subsequently in Cambridge University, UK. Between 2004 and 2006 he worked in medical imaging at Siemens Corporate Research, Princeton, USA. From 2006 to 2011 he was Lecturer and then Reader at Heriot-Watt University and Honorary Fellow at the University of Edinburgh, UK. His research interests are in computer vision, machine learning, computational photography, and image processing.