End 2 End

End-to-end (E2E) systems aim to simplify complex processes by integrating multiple stages into a single, unified model, thereby improving efficiency and performance. Current research focuses on applying E2E approaches across diverse fields, including speech recognition, natural language understanding, autonomous driving, and medical image analysis, often employing deep learning architectures like transformers and recurrent neural networks. This approach is significant because it streamlines workflows, reduces error propagation, and enables the development of more robust and efficient systems for various applications. The resulting improvements in accuracy, speed, and resource utilization are driving widespread adoption across multiple scientific and industrial domains.