Multi Scale Training

Multi-scale training in deep learning aims to improve model robustness and efficiency by training on images or data at varying resolutions, rather than a single fixed size. Current research focuses on developing training strategies that effectively leverage this multi-resolution data, including techniques like variable batch size samplers and geometry-aligned approaches, often applied within transformer and autoencoder architectures. This approach leads to improved accuracy, better calibration, and faster training across diverse applications, from satellite imagery analysis and object detection to solving partial differential equations and high-resolution image synthesis. The resulting models demonstrate enhanced performance and generalization capabilities compared to single-scale trained counterparts.