Low Complexity Model

Low-complexity models aim to achieve high performance with minimal computational resources, a crucial objective in deploying machine learning models on resource-constrained devices or for real-time applications. Current research focuses on developing efficient architectures, such as modified MobileNets and EfficientNets, and employing techniques like knowledge distillation (teacher-student training) and model quantization to reduce model size and computational demands while maintaining accuracy. This pursuit of efficiency is driving advancements in diverse fields, including acoustic scene classification, remote sensing image classification, and causal inference through efficient DAG learning algorithms, ultimately impacting the feasibility and scalability of AI applications.