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.

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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

**(Select all that apply.)** Which is a distillation-based algorithm? 

(Select all that apply.) Which is a distillation-based algorithm?

**(Select all that apply.)** How does distillation ensure similarity maximization while avoiding trivial solutions? 

(Select all that apply.) How does distillation ensure similarity maximization while avoiding trivial solutions?

SimCLR samples negatives from the training batch itself.

SimCLR uses a dictionary to maintain a large number of negative samples.

SimCLR is a contrastive learning algorithm.

**(Select all that apply.)** Which is/are true about the SimCLR algorithm? 

(Select all that apply.) Which is/are true about the SimCLR algorithm?

It ensures that the keys enqueued at later points of time are consistent with keys enqueued quite early. 

It improves the quality of negative samples.

It ensures learning asymmetry between the key and query encoders, which is necessary for contrastive learning.

It prevents the computational graph from exploding.

**(Select all that apply.)** How is momentum contrast in the MoCo-v2 algorithm useful? 

(Select all that apply.) How is momentum contrast in the MoCo-v2 algorithm useful?

Predicting the cluster assignments and updating network parameters

Clustering the network features and generating cluster assignments

The first step in the DeepCluster algorithm involves:

The first step in the DeepCluster algorithm involves:

The main difference between BYOL and SimSiam algorithm is:

The main difference between BYOL and SimSiam algorithm is:

**(Select all that apply.)** Which algorithm(s) use(s) an exponential moving average strategy to update some network parameters? 

(Select all that apply.) Which algorithm(s) use(s) an exponential moving average strategy to update some network parameters?

Quiz on similarity maximization and redundancy reduction

Quiz on similarity maximization and redundancy reduction

Quiz on similarity maximization and redundancy reduction.

Introduction to Self-Supervised Learning

Pretext Tasks

Similarity Maximization and Redundancy Reduction

Masked Image Modeling

Appendix

Quiz on Similarity Maximization and Redundancy Reduction