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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.

Mastering Self-Supervised Algorithms for Learning without Labels

The framework of masked image modeling consists of:

The framework of masked image modeling consists of:

Which algorithms feed only visible (unmasked) patches to their encoder layer?

Which algorithms feed only visible (unmasked) patches to their encoder layer?

To project image patches to their respective patch embeddings.

To summarize the information present in all patches in one single representation.

To provide positional information about the location of patches in the image.

What is the purpose of positional encodings in a vision transformer architecture?

What is the purpose of positional encodings in a vision transformer architecture?

**(Select all that apply.)** Masked Siamese Networks use which loss function(s) or regularizer(s)? 

(Select all that apply.) Masked Siamese Networks use which loss function(s) or regularizer(s)?

**(Select all that apply.)** In which aspects do the SimMIM algorithm and the MAE algorithm differ? 

(Select all that apply.) In which aspects do the SimMIM algorithm and the MAE algorithm differ?

It encourages the model to utilize the full set of prototypes.

It's the negative entropy of average prediction across all the anchor views. 

It's the cross-entropy between anchor predictions and target predictions.

It encourages the model to minimize the distributional difference between anchor and target predictions.

**(Select all that apply.)** Which statement(s) is/are true about the ME-MAX regularizer in MSN? 

(Select all that apply.) Which statement(s) is/are true about the ME-MAX regularizer in MSN?

Introduction to Self-Supervised Learning

Pretext Tasks

Similarity Maximization and Redundancy Reduction

Masked Image Modeling

Appendix

Quiz