Active Learning
Active learning is a machine learning paradigm focused on optimizing data labeling efficiency by strategically selecting the most informative samples for annotation from a larger unlabeled pool. Current research emphasizes developing novel acquisition functions and data pruning strategies to reduce computational costs associated with large datasets, exploring the integration of active learning with various model architectures (including deep neural networks, Gaussian processes, and language models), and addressing challenges like privacy preservation and handling open-set noise. This approach holds significant promise for reducing the substantial cost and effort of data labeling in diverse fields, ranging from image classification and natural language processing to materials science and healthcare.
Papers
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data
Amir Namavar Jahromi, Ebrahim Pourjafari, Hadis Karimipour, Amit Satpathy, Lovell Hodge
Continuous Learning for Android Malware Detection
Yizheng Chen, Zhoujie Ding, David Wagner
Combining self-labeling and demand based active learning for non-stationary data streams
Valerie Vaquet, Fabian Hinder, Johannes Brinkrolf, Barbara Hammer
Best Practices in Active Learning for Semantic Segmentation
Sudhanshu Mittal, Joshua Niemeijer, Jörg P. Schäfer, Thomas Brox