Self-supervised learning (SSL) is a subset of unsupervised learning where the model generates its own labels from raw data to learn patterns. SSL avoids the need for costly labeled datasets, making machine learning more efficient. It is beneficial in various applications including computer vision, natural language processing, and speech recognition. Though resource-intensive, SSL offers significant advantages by reducing the reliance on labeled data and improving model performance.
Table of contents
A Deep Dive into Self-Supervised LearningWhat is Self-Supervised Learning?How does self-supervised learning work?Why Is Self-Supervised Learning Important?Applications of Self-Supervised LearningConclusion222 Impressions