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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.

Advanced

31 Lessons

Certificate of Completion

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This course includes

41 Playgrounds

Course Overview

What You'll Learn

Course Content

Course Overview

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...Show More

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

Course Content

Introduction to Self-Supervised Learning

3 Lessons

Get familiar with self-supervised learning, leveraging unlabeled data for adaptable model training.

Course Overview and Prerequisites

Introduction to Self-Supervised Learning

Introductory Quiz on Self-Supervised Learning

Pretext Tasks

6 Lessons

Unpack the core of self-supervised learning through pretext tasks like rotation, positioning, and puzzles.

Introduction to Pretext Tasks

Predicting Relative Position of Patches

Predicting Rotation

Solving Jigsaw Puzzles

Quiz on Pretext Tasks

Coding Exercise on Pretext Tasks

Similarity Maximization and Redundancy Reduction

12 Lessons

Examine techniques for similarity maximization and redundancy reduction through modern self-supervised learning algorithms.

Introduction to Similarity Maximization

Contrastive Learning: The SimCLR Algorithm

SimCLR Training Objective

The MoCo-v2 Algorithm

Clustering: The DeepCluster Algorithm

DeepCluster Training

Distillation: The BYOL Algorithm

BYOL Training

The SimSiam Algorithm

Barlow Twins

Coding Exercise on Barlow Twins

Masked Image Modeling

9 Lessons

Grasp the fundamentals of masked image modeling techniques and their applications in self-supervised learning.

Introduction to Masked Image Modeling

Simple Masked Image Modeling

Masked Autoencoders: Masking and Encoder

Masked Autoencoders: Decoder and Loss Function

Masked Siamese Networks: Masking Strategy and Encoder

Masked Siamese Networks: Objective and Training

Coding Exercise on Masked Modeling

Quiz

Conclusion

Appendix

1 Lessons

Dig into key research papers on self-supervised learning advancements and techniques.

References for Research Papers

Course Author

Puneet Mangla

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