Participants and instructors attending the NORA summer school.
Image:
Ieva Dundulienė

Participants and instructors attending the NORA summer school.

Summer course on self-supervised learning

Visual Intelligence-lead course on self-supervised learning as part of the NORA summer school 2023

Visual Intelligence-lead course on self-supervised learning as part of the NORA summer school 2023

Visual Intelligence researchers Michael Kampffmeyer and Kristoffer Wickstrøm were responsible for hosting one of the tracks during this year's NORA summer school, which introduced participants to the fundamental and very recent developments in self-supervised learning. Self-supervised learning is one of the most popular topics within contemporary artificial intelligence research, and constitutes the foundation for recent algorithms like ChatGPT.

The key idea of self-supervised learning is to learn a meaningful representation of complex data. Learning a meaningful representation from data without human supervision is a well established problem in machine learning. However, recent works on self-supervised representation learning have achieved impressive performance on a wide range of different tasks involving data types such as images, time series, and text. In some cases, the performance in these unsupervised method can even rival their supervised counterparts. Self-supervised approaches learn representations in a similar manner as in supervised learning, but creates the labels from unlabeled datasets. How to produce the labels from unlabeled datasets and how to formulate a self-supervised loss are key components in self-supervised learning.

The Visual Intelligence-lead course had participants from both national and international universities, as well as participants from industrial partners. Over five days of exciting lectures the participants were actively involved in both theoretical and practical discussion, which successfully connected young Visual Intelligence researchers with potential collaborators from across the world.

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