Streaming Model

Streaming models process data sequentially, one item at a time, aiming to minimize memory usage and latency, making them crucial for real-time applications. Current research focuses on improving the accuracy and efficiency of these models across diverse tasks, including speech processing (using architectures like Conformers and RNN-Transducers), adversarial robustness, and clustering, often employing techniques like chunking, caching, and anchor loss to enhance performance. This field is significant because it enables efficient analysis of massive datasets in resource-constrained environments, with applications ranging from autonomous driving and smart manufacturing to financial fraud detection and personalized healthcare.