Representation Learning

Representation learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and handle. These representations can be designed to be interpretable, reveal hidden features, or be used for transfer learning. They hold significant value in fundamental tasks such as image classification and retrieval. Supervised representation learning leverages labeled data to learn representations that can solve other tasks; unsupervised representation learning, on the other hand, learns representations through unlabeled data, reducing the need for labeled data when tackling new tasks. In recent years, self-supervised learning has become a major driving force behind unsupervised representation learning, especially in the fields of computer vision and natural language processing.