title: Active learning for noisy oracle via density power divergence.
abstract: The accuracy of active learning is critically influenced by the existence of noisy labels given by a noisy oracle. In this paper, we propose a novel pool-based active learning framework through robust measures based on density power divergence. By minimizing density power divergence, such as β-divergence and γ-divergence, one can estimate the model accurately even under the existence of noisy labels within data. Accordingly, we develop query selecting measures for pool-based active learning using these divergences. In addition, we propose an evaluation scheme for these measures based on asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation error directly. Experiments with benchmark datasets and real-world image datasets show that our active learning scheme performs better than several baseline methods.
authors.affiliation: The Institute of Scientific and Industrial Research, Osaka University, 8-1, Mihogaoaka, Ibaraki, Osaka, Japan. sogawa@ar.sanken.osaka-u.ac.jp
authors.forename: Yasuhiro
authors.initials: Y
authors.lastname: Sogawa
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Ueno
authors.forename: Yoshinobu
authors.initials: Y
authors.lastname: Kawahara
authors.forename: Takashi
authors.initials: T
authors.lastname: Washio
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D018794:Problem-Based Learning / Q000379:methods
keywords: Active learning; Density power divergence; Noisy oracle