Abstract
In active learning, training data to be annotated is generally selected based on the confidence scores output by a classifier. However, the selection of the initial training data pool typically relies on random sampling since the classifier has not yet been trained. To address this issue, it is necessary to obtain a classifier that can appropriately assess confidence without supervised learning at the initial pool selection stage. In this study, we propose an unsupervised confidence calibration method based on domain adaptation. The proposed framework integrates a variational autoencoder into an unsupervised domain adaptation method and trains them simultaneously, thereby allowing the estimation of the joint distribution of input data and class labels. The proposed framework enables the classifier to compute reliable confidence scores for the target domain in a completely unsupervised manner. We conducted experiments on medical image datasets to evaluate the effectiveness of the proposed unsupervised confidence calibration method and its impact on active learning. Experimental results showed that the proposed method consistently outperforms the baseline in all six transfer settings, achieving an average improvement of 1.0–1.5 points in classification accuracy across domains.
Naoya Fukui
Graduate Student
Hideaki Hayashi
Associate Professor
Hideaki Hayashi is an associate professor with D3 Center, the University of Osaka. His research interests focus on neural networks, machine learning, and medical data analysis.
Hajime Nagahara
Professor
He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.