Based Active Learning

Based active learning aims to improve the efficiency of training machine learning models by strategically selecting the most informative unlabeled data points for annotation. Current research focuses on developing novel query strategies, such as those based on model prediction inconsistencies or uncertainty measures, often employing deep learning architectures. These advancements address limitations of traditional active learning, particularly in open-set scenarios and complex tasks like medical image segmentation, leading to significant reductions in annotation costs and improved model performance with limited labeled data. This has broad implications for various fields requiring large labeled datasets, including healthcare and computer vision.

Papers

January 18, 2024

Querying Easily Flip-flopped Samples for Deep Active Learning
Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, Chang D. Yoo
Active Learning Query Information Predictive Uncertainty External Sample Decision Boundary Deep Active Learning Active Learning Algorithm Based Active Learning

January 10, 2024

Inconsistency-Based Data-Centric Active Open-Set Annotation
Ruiyu Mao, Ouyang Xu, Yunhui Guo
Active Learning Open Set Based Active Learning

August 9, 2023

Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
Fan Bai, Xiaohan Xing, Yutian Shen, Han Ma, Max Q. -H. Meng
Active Learning Class Activation Wireless Capsule Endoscopy Based Active Learning

June 29, 2023

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification
Tao Wang, Xinlin Zhang, Yuanbo Zhou, Junlin Lan, Tao Tan, Min Du, Qinquan Gao, Tong Tong
Classification Code Active Learning Medical Image Segmentation Medical Image Classification Medical Image Datasets PCa Detection Small Perturbation Based Active Learning

June 21, 2023

Multi-Task Consistency for Active Learning
Aral Hekimoglu, Philipp Friedrich, Walter Zimmer, Michael Schmidt, Alvaro Marcos-Ramiro, Alois C. Knoll
Semantic Segmentation Active Learning Task Consistency Multi Task Network Based Active Learning

June 2, 2023

Agnostic Multi-Group Active Learning
Nick Rittler, Kamalika Chaudhuri
Agnostic Learning Based Active Learning Multi Group Learning

June 7, 2022

Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning
Jiannan Guo, Yangyang Kang, Yu Duan, Xiaozhong Liu, Siliang Tang, Wenqiao Zhang, Kun Kuang, Changlong Sun, Fei Wu
Active Learning Semi Supervised Learning Data Labeling Advanced Machine Learning Based Active Learning Edge Consistency Agent Orchestration