Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch and Bernhard Sick have published an article titled Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification at the conference “European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)”. This is the topic of the article:

Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. \\\\textsc{Bait}, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets. However, \\\\textsc{Bait}'s high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting \\\\textsc{Bait} in their evaluation. This paper introduces two methods to enhance \\\\textsc{Bait}'s computational efficiency and scalability. Notably, we significantly reduce its time complexity by approximating the Fisher Information. In particular, we adapt the original formulation by i) taking the expectation over the most probable classes, and ii) constructing a binary classification task, leading to an alternative likelihood for gradient computations. Consequently, this allows the efficient use of \\\\textsc{Bait} on large-scale datasets, including ImageNet. Our unified and comprehensive evaluation across a variety of datasets demonstrates that our approximations achieve strong performance with considerably reduced time complexity. Furthermore, we provide an extensive open-source toolbox that implements recent state-of-the-art AL strategies, available at \\\\url{https://github.com/dhuseljic/dal-toolbox}.

Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch and Bernhard Sick have published an article titled Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification at the conference “European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)”. This is the topic of the article:

Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. \\\\textsc{Bait}, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets. However, \\\\textsc{Bait}'s high computational and memory requirements hinder its applicability on large-scale classification tasks, resulting in current research neglecting \\\\textsc{Bait} in their evaluation. This paper introduces two methods to enhance \\\\textsc{Bait}'s computational efficiency and scalability. Notably, we significantly reduce its time complexity by approximating the Fisher Information. In particular, we adapt the original formulation by i) taking the expectation over the most probable classes, and ii) constructing a binary classification task, leading to an alternative likelihood for gradient computations. Consequently, this allows the efficient use of \\\\textsc{Bait} on large-scale datasets, including ImageNet. Our unified and comprehensive evaluation across a variety of datasets demonstrates that our approximations achieve strong performance with considerably reduced time complexity. Furthermore, we provide an extensive open-source toolbox that implements recent state-of-the-art AL strategies, available at \\\\url{https://github.com/dhuseljic/dal-toolbox}.