Instance Level Label

Instance-level labeling in machine learning focuses on assigning labels to individual data points within a dataset, rather than just to the entire dataset or groups of data points. Current research emphasizes developing methods that effectively utilize limited or noisy instance-level labels, often employing techniques like contrastive learning, attention mechanisms, and weakly supervised learning approaches within various model architectures including transformers and convolutional neural networks. This area is crucial for improving the efficiency and accuracy of machine learning models across diverse applications, particularly in scenarios with high annotation costs or inherent label ambiguity, such as medical image analysis and document processing.