How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning

Gilhuber, Sandra; Hvingelby, Rasmus; Fok, Mang Ling Ada und Seidl, Thomas (2023): How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 18.-22. September 2023. Koutra, Danai; Plant, Claudia; Gomez Rodriguez, Manuel; Baralis, Elena und Bonchi, Francesco (Hrsg.): In: Machine Learning and Knowledge Discovery in Databases: Research Track, Lecture Notes in Artificial Intelligence Bd. 14170 Cham: Springer. S. 330-347

Abstract

Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by results showing that combining semi-supervised learning (SSL) methods with a random selection for labeling can outperform existing active learning (AL) techniques. However, these results are obtained from experiments on well-established benchmark datasets that can overestimate the external validity. However, the literature lacks sufficient research on the performance of active semi-supervised learning methods in realistic data scenarios, leaving a notable gap in our understanding. Therefore we present three data challenges common in real-world applications: between-class imbalance, within-class imbalance, and between-class similarity. These challenges can hurt SSL performance due to confirmation bias. We conduct experiments with SSL and AL on simulated data challenges and find that random sampling does not mitigate confirmation bias and, in some cases, leads to worse performance than supervised learning. In contrast, we demonstrate that AL can overcome confirmation bias in SSL in these realistic settings. Our results provide insights into the potential of combining active and semi-supervised learning in the presence of common real-world challenges, which is a promising direction for robust methods when learning with limited labeled data in real-world applications.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-031-43414-3 ; 978-3-031-43415-0 ; 978-3-031-43416-7
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch