Regression Representation

Regression representation learning aims to create feature representations that accurately reflect the continuous nature of regression targets, unlike classification which focuses on discrete class separation. Recent research emphasizes developing methods that explicitly enforce the ordinality of the target variable within the learned representation, addressing issues like fragmented representations and poor generalization seen in traditional end-to-end approaches. This is achieved through techniques like contrastive learning, which compares samples based on their target rankings, and by incorporating topological constraints to ensure the representation space aligns with the target space's structure. Improved regression representations have significant implications for various fields, including drug discovery and computer vision, by enhancing model accuracy, robustness, and efficiency.