Distribution Classification

Distribution classification focuses on improving the ability of machine learning models to accurately classify data points as belonging to known classes (in-distribution) while reliably identifying data that originates from unknown classes (out-of-distribution). Current research emphasizes robust methods for handling class imbalances (long-tailed distributions) and open-set scenarios where unseen classes are present in the unlabeled data, often employing techniques like contrastive learning, expert weighting, and selective data usage to enhance both in-distribution accuracy and out-of-distribution detection. This work is crucial for building more reliable and trustworthy AI systems, particularly in applications where encountering unexpected data is common, such as medical image analysis and natural language processing.