Explore a detailed explanation of the BYOL (Bootstrap Your Own Latent) approach to self-supervised learning in this 34-minute video. Dive into the innovative technique that achieves state-of-the-art performance without relying on negative samples. Learn about image representation learning, self-supervised learning, and the challenges associated with negative samples. Understand the BYOL algorithm, its implementation, and the experimental results that demonstrate its effectiveness. Examine the broader impact and conclusions drawn from this groundbreaking research in computer vision and machine learning.
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning - Paper Explained
Overview
Syllabus
- Intro & Overview
- Image Representation Learning
- Self-Supervised Learning
- Negative Samples
- BYOL
- Experiments
- Conclusion & Broader Impact
Taught by
Yannic Kilcher
Related Courses
-
Big Self-Supervised Models Are Strong Semi-Supervised Learners
-
Multiview and Self-Supervised Representation Learning - Nonlinear Mixture Identification
-
Emerging Properties in Self-Supervised Vision Transformers - Lecture 25
-
Emerging Properties in Self-Supervised Vision Transformers - Facebook AI Research Explained
-
Machine Learning: Clustering & Retrieval
4.6 -
Self-supervised Deep Learning for Image Recovery - SIAM-IS Virtual Seminar
