Authors:
Siteng Ma
1
;
Yu An
1
;
Jing Wang
2
;
Aonghus Lawlor
1
and
Ruihai Dong
1
Affiliations:
1
The Insight Centre for Data Analytics, School of Computer Science, University College Dublin, Dublin, Ireland
;
2
College of Computer Science, North China Institute of Aerospace Engineering, Langfang, China
Keyword(s):
Deep Learning, Active Learning, Adversarial Attack, Counterfactual Sample, Medical Image Classification.
Abstract:
Active learning (AL) is a subset of machine learning, which attempts to minimize the number of required training labels while maximizing the performance of the model. Most current research directions regarding AL focus on the improvement of query strategies. However, efficiently utilizing data may lead to more performance improvements than are thought to be achievable by changing the selection strategy. Thus, we present an adaptive adversarial sample-based approach to query unlabeled samples close to the decision boundary through the adversarial attack. Notably, based on that, we investigate the importance of using existing data effectively in AL by integrating generated adversarial samples according to consistency regularization and leveraging large numbers of unlabeled images via pseudo-labeling with the oracle-annotated instances during training. In addition, we explore an adaptive way to request labels dynamically as the model changes state. The experimental results verify our fr
amework’s effectiveness with a significant improvement over various state-of-the-art methods for multiple medical applications. Our method achieves 3% above the supervised learning accuracy on the Messidor Dataset (the task of Diabetic Retinopathy detection) using only 34% of the whole dataset. We also conducted an extensive study on a histological Breast Cancer Diagnosis Dataset. Our code is available at https://github.com/HelenMa9998/adversarial active learning.
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