Self-Supervised Learning: Self-Prediction and Contrastive Learning | Tutorial | NeurIPS 2021 

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In the world of artificial intelligence, self-supervised learning is a game-changing technique for training models using unlabelled data. Self-prediction and contrastive learning are two popular methods of self-supervised learning that have been successful in various applications, such as image and speech recognition.
In this video, we will dive deep into the concepts of self-supervised learning, self-prediction, and contrastive learning. We will explain how these techniques work, and explore their advantages over traditional supervised learning methods.
You will learn about the key components of self-supervised learning, such as pretext tasks and feature extraction, and see how they enable models to learn from unlabelled data. We will also provide examples of real-world applications of self-supervised learning, including the popular BERT model for natural language processing.
So, whether you are a beginner in AI or an experienced practitioner, this video will provide you with valuable insights into the world of self-supervised learning.
Keywords:
Self-supervised learning, self-prediction, contrastive learning, unsupervised learning, pretext tasks, feature extraction, BERT model, natural language processing, artificial intelligence, machine learning.