Feature Richness Enhancement

Feature richness enhancement aims to improve the performance of machine learning models by increasing the informativeness and relevance of the features used for prediction. Current research focuses on methods to achieve this, including techniques that leverage contrastive learning, manipulate feature representation rank, and incorporate handcrafted features alongside learned representations to mitigate issues like catastrophic forgetting and spurious correlations. These advancements are significant because they lead to more robust and accurate models across various applications, such as image classification, natural language processing, and semantic segmentation, particularly in scenarios with imbalanced datasets or out-of-distribution data.