Local to Global Learning

Local-to-global learning is a machine learning paradigm that integrates local feature extraction with global context understanding to improve model performance and generalization. Current research focuses on applying this approach in diverse areas, including image classification (e.g., medical image analysis and driving scene segmentation), leveraging techniques like self-supervised learning, contrastive learning, and 3D convolutions within various model architectures. This strategy enhances the ability of models to handle complex data, particularly those with small or noisy features, leading to improved accuracy and efficiency in tasks ranging from medical diagnosis to scene understanding.