Inlier Classification

Inlier classification focuses on identifying data points belonging to the expected distribution within a dataset, distinguishing them from outliers. Current research emphasizes robust methods for inlier identification across diverse applications, employing techniques like deep learning architectures (e.g., autoencoders, deep sets), Bayesian networks, and RANSAC-based algorithms, often incorporating probabilistic scoring or weighted sampling strategies to improve accuracy and efficiency. These advancements are crucial for improving the reliability and robustness of various machine learning models, particularly in computer vision, anomaly detection, and medical image analysis, where accurate inlier classification is essential for reliable results and fair decision-making.