Efficient and Scalable Deep Active Image Classification through Approximated Bait

Bait, a deep active learning strategy based on Fisher Information, demonstrates superior performance but suffers from high computational and memory requirements. This work introduces two approximation methods, Bait (Exp) and Bait (Binary), to enhance Bait's computational efficiency and scalability across large-scale image datasets without compromising performance.