Self-Supervised Learning

Self-supervised learning is a machine learning paradigm where models learn representations from unlabeled data by creating their own supervisory signals from the data itself, typically through pretext tasks like predicting missing parts or transformations. It bridges the gap between unsupervised learning (no labels) and supervised learning (full labels), enabling models to leverage vast amounts of unlabeled data for pre-training before fine-tuning on labeled tasks. This approach is widely used in domains like natural language processing and computer vision to improve performance with limited labeled data.

Also known as: SSL, Self-Supervised, Self-Supervised Representation Learning, Self-Supervised Pre-training, Contrastive Learning (a common technique)

Why learn Self-Supervised Learning?

Developers should learn self-supervised learning when working with large datasets that have little or no labeled data, as it reduces annotation costs and improves model generalization in fields like NLP (e.g., BERT, GPT) and computer vision (e.g., contrastive learning). It's particularly valuable for transfer learning scenarios, where pre-trained models can be fine-tuned for specific downstream tasks, enhancing efficiency and accuracy in applications such as text classification, image recognition, and speech processing.

Learning Resources

• docs: Self-Supervised Learning: The Dark Matter of Intelligence
• video: Self-Supervised Learning Tutorial by Yann LeCun
• tutorial: Self-Supervised Learning in Computer Vision - Papers With Code
• course: Self-Supervised Learning Course on Coursera
• docs: Self-Supervised Learning: A Survey