Representation Learning

Representation learning is a process in machine learning where algorithms extract meaningful patterns from raw data to generate more comprehensible and manageable data representations. These representations can be designed to be interpretable, revealing hidden features, or used for transfer learning, which is of significant value for fundamental tasks such as image classification and retrieval. Deep neural networks, as models for representation learning, typically encode information and project it into different subspaces before passing it to a linear classifier for training. Representation learning can be categorized into supervised representation learning and unsupervised representation learning. The former uses labeled data to learn representations that help solve other tasks, while the latter 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, finding extensive applications in computer vision and natural language processing.