5.0
Advanced
7h
Mastering Self-Supervised Algorithms for Learning without Labels
Gain insights into self-supervised learning. Delve into pseudo label generation, similarity maximization, redundancy reduction, and masked image modeling to apply and modify these algorithms on unlabelled datasets.
This course covers self-supervised algorithms, which are useful for large pools of unlabelled data or when obtaining a high-quality labeled dataset is difficult. These algorithms leverage the supervisory signals from the structure of the unlabeled data to predict any unobserved or hidden property of the input.
You’ll start with the fundamentals of self-supervised learning and then implement your first class of algorithms. You’ll learn to generate pseudo labels and use these labels for training models using supervised learning. Next, you’ll learn about similarity maximization-based self-supervised algorithms. You’ll also look into redundancy reduction, which reduces the redundancy in the feature representations while maximizing the similarity between similar images. Lastly, you’ll learn to implement masked image modeling.
After learning all this, you'll be able to apply the self-supervised models to unlabelled datasets. Furthermore, you’ll be able to implement and modify existing self-supervised algorithms.
This course covers self-supervised algorithms, which are useful for large pools of unlabelled data or when obtaining a high-qual...Show More
WHAT YOU'LL LEARN
An understanding of self-supervised learning and its advantage over unsupervised learning
Working knowledge of designing your self-supervised learning tasks/objectives
Hands-on experience implementing and modifying existing self-supervised learning objectives to learn from unlabelled data
Ability to transfer and evaluate your self-supervised network representations on a downstream task
Familiarity with core components of self-supervised learning, including pretext tasks, similarity maximization, redundancy reduction, and masked image modeling
An understanding of self-supervised learning and its advantage over unsupervised learning
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Content
1.
Introduction to Self-Supervised Learning
3 Lessons
Get familiar with self-supervised learning, leveraging unlabeled data for adaptable model training.
2.
Pretext Tasks
6 Lessons
Unpack the core of self-supervised learning through pretext tasks like rotation, positioning, and puzzles.
3.
Similarity Maximization and Redundancy Reduction
12 Lessons
Examine techniques for similarity maximization and redundancy reduction through modern self-supervised learning algorithms.
4.
Masked Image Modeling
9 Lessons
Grasp the fundamentals of masked image modeling techniques and their applications in self-supervised learning.
5.
Appendix
1 Lessons
Dig into key research papers on self-supervised learning advancements and techniques.
Certificate of Completion
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Course Author:
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