2022/07/27 by Zhanpeng Feng, Feng, Zhanpeng, Shiliang Zhang +13 · 3 citations
Computer Science · #Active learning (machine learning) #Advanced Neural Network Applications #Annotation #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #FOS: Computer and information sciences #Field (mathematics) #Learning object #Machine Learning and Algorithms #Machine Learning and Data Classification #Machine learning #Object (grammar) #Object detection #Pattern recognition (psychology) #cs.CV
paper · pdf · doi:10.48550/arxiv.2207.13339
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/07/27 · arxiv created 2022/11/24 · arxiv updated 2022/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architecture design, active learning aims at automating training data selection. It is especially critical for training a long-tailed task, in which positive samples are sparsely distributed. Active learning alleviates the expensive data annotation issue through incrementally training models powered with efficient data selection. Instead of annotating all unlabeled samples, it iteratively selects and annotates the most valuable samples. Active learning has been popular in image classification, but has not been fully explored in object detection. Most of current approaches on object detection are evaluated with different settings, making it difficult to fairly compare their performance. To facilitate the research in this field, this paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection. Developed on an automatic deep model training system, this ALBench framework is easy-to-use, compatible with different active learning algorithms, and ensures the same training and testing protocols. We hope this automated benchmark system help researchers to easily reproduce literature's performance and have objective comparisons with prior arts. The code will be release through Github.