End to End Model

End-to-end models aim to perform complex tasks in a single, integrated system, eliminating the error propagation and efficiency limitations of traditional multi-stage pipelines. Current research focuses on applying this approach to diverse areas, including speech recognition, image processing, natural language processing, and time series analysis, often employing transformer-based architectures and leveraging techniques like knowledge distillation and multimodal learning. The resulting improvements in accuracy, speed, and resource efficiency have significant implications for various fields, ranging from medical diagnosis to autonomous driving.