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Unit 7.7 Using Unlabeled Data with Self-Supervised

Slides

Part 1: Transferring Knowledge From Large Datasets
Part 2: Different Types of Self-Supervised Learning
Part 3: Self-Supervised Learning with SimCLR

References

Chen, Kornblith, Norouzi, and Hinton (2020). A Simple Framework for Contrastive Learning of Visual Representations

Code

Parts 4 and 5, 7.7-self-supervised

What we covered in this video lecture

In this series of videos, we discussed self-supervised learning, which lets us leverage unlabeled data for pretraining. We also discussed the two broad subcategories of self-supervised learning, self-prediction and contrastive learning. Then, to implement a contrastive learning method in practice, we looked more closely at SimCLR

By the way, the overal concept behind self-supervised learning is also responsible for the success of ChatGPT, but more on large language models in Unit 8!

Additional resources if you want to learn more

SimCLR is one of the most successful and popular methods for contrastive learning. However, there are many, many other self-supervised learning techniques out there. For an overview, I recommend A survey on contrastive self-supervised learning and Advances in Understanding, Improving, and Applying Contrastive Learning. And for an example of a non-contrastive self-supervised learning technique, I recommend Masked Autoencoders Are Scalable Vision Learners.

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Quiz: 7.7 Using Unlabeled Data with Self-Supervised - Part 1

What is the main idea behind self-supervised learning?

Learning from a large amount of labeled data

Incorrect. This is related to supervised learning, not self-supervised learning.

Learning from a large amount of unlabeled data by creating artificial labels

Correct. So, in this sense, the model learns to solve these tasks without explicit supervision, hence, “self”-supervised.

Using multiple models to make predictions

Incorrect. This describes ensemble methods, not self-supervised learning.

Reducing the number of layers in a neural network

Incorrect. This is not related to the main idea behind self-supervised learning.

Please answer all questions to proceed.

Quiz: 7.7 Using Unlabeled Data with Self-Supervised - Part 2

What is the primary advantage of contrastive learning over supervised learning methods?

They are more interpretable

Incorrect. Contrastive learning methods are not inherently more interpretable than supervised learning methods.

They perform better with limited labeled data

Correct. Contrastive learning methods can perform better with limited labeled data compared to supervised learning methods, as they learn useful representations from large amounts of unlabeled data.

They require fewer computational resources

Incorrect. Contrastive learning methods are not inherently more interpretable than supervised learning methods.

They always outperform supervised learning methods

Incorrect. While contrastive learning methods can perform better with limited labeled data, they do not always outperform supervised learning methods in all scenarios.

Please answer all questions to proceed.

Quiz: 7.7 Using Unlabeled Data with Self-Supervised - Part 3

In SimCLR, what is the primary objective during training?

Minimizing the distance between different data samples

Incorrect. The goal is to maximize the distance between different data samples, not minimize it.

Maximizing the distance between augmented views of the same data sample

Incorrect. The goal is to minimize the distance between augmented views of the same data sample, not maximize it.

Minimizing the distance between augmented views of the same data sample

Correct. We minimize the distance between representations of augmented views of the same data sample while maximizing the distance between representations of different data samples.

Maximizing the distance between all data samples

Incorrect. The goal is to maximize the distance between different data samples while minimizing the distance between augmented views of the same data sample

Please answer all questions to proceed.

Quiz: 7.7 Using Unlabeled Data with Self-Supervised - Part 4 & 5

Which of the following is NOT a viable/suitable method to evaluate the quality of learned representations in self-supervised learning?

Monitoring the loss value during training

Incorrect. Monitoring the loss value during training can provide insights into the model’s progress and the quality of the learned representations.

Directly measuring accuracy during training

Correct. Directly measuring accuracy during training, in self-supervised learning, is not possible to directly measure accuracy during training because there are no ground truth labels to compare with the model’s predictions.

Training a linear classifier on the learned representations and evaluating the accuracy of that classifier.

Incorrect. This is a suitable method to evaluate the quality of learned representations in self-supervised learning.

Fine-tuning the model on a labeled downstream task

Incorrect. This is a suitable method to evaluate the quality of learned representations in self-supervised learning.

Please answer all questions to proceed.

Watch Video 1 Mark complete and go to Unit 7 Exercises →

Unit 7.7

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