Lightweight Classifier

Lightweight classifiers aim to create efficient machine learning models capable of performing classification tasks on resource-constrained devices. Current research focuses on techniques like model compression (e.g., pruning, knowledge distillation), the use of pre-trained models (including vision-language models), and the development of novel architectures such as modified MobileNets and efficient Vision Transformers. These advancements are crucial for deploying deep learning in applications with limited computational power and memory, such as mobile devices and edge computing, impacting fields ranging from sustainable resource management to communication systems.