Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification

arXiv:2404.08981v1 Announce Type: cross
Abstract: Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. BAIT, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets. However, BAIT's high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting BAIT in their evaluation. This paper introduces two methods to enhance BAIT's computational efficiency and scalability. Notably, we significantly reduce its time complexity …

arxiv classification cs.cv cs.lg fishing image scalable type