Dynamic neural networks

Dynamic neural networks are adaptable models that can adjust their structure or parameters based on input complexity or computational constraints during training or inference. Compared to static architectures, dynamic neural networks offer higher efficiency, adaptability, and scalability, optimizing resource utilization and enhancing model performance. They are widely used in complex and variable task scenarios.