Patch Level Representation

Patch-level representation learning focuses on extracting meaningful features from smaller segments of data, such as image patches or time-series segments, to improve the performance of various machine learning models. Current research emphasizes the use of transformer networks and self-supervised learning techniques to effectively capture spatial and temporal relationships between patches, often within a multi-patch prediction framework. This approach has shown significant improvements in diverse applications, including video anomaly detection, human activity recognition, and medical image analysis, by enabling more efficient and accurate processing of high-dimensional data.