Conclusion
Explore a comprehensive summary of self-supervised learning techniques covered in this course, including pretext tasks, similarity maximization, redundancy reduction, and masked image modeling. Understand how these methods help learn rich representations from unlabeled data and enable applying and adapting self-supervised algorithms effectively.
We'll cover the following...
In this course, we covered a popular AI paradigm called self-supervised learning that aims to learn rich representations from a huge pool of unlabeled data so those representations are transferrable to downstream tasks.
Pretext tasks
We formulated the framework and taxonomy of self-supervised learning and discussed the first class of algorithms based on pretext tasks. Pretext task-based self-supervised learning generates pseudo labels from the input structure and uses these labels for ...