用少量标注数据+主动学习，高效发现罕见异常目标
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
- 结合半监督与主动学习，用有限标签训练异常检测模型
- 仅需5-10个标注样本，即达0.96（miniImageNet）AUROC
- 适合天文、视觉等标签稀缺领域的异常发现任务
在大规模数据集中的异常检测对天文学和计算机视觉至关重要。然而，由于标注数据稀缺，传统监督方法难以应用。我们提出AnomalyMatch，一个结合半监督FixMatch算法与主动学习的异常检测框架，采用EfficientNet分类器，专为大规模应用设计，并集成至欧洲航天局ESA Datalabs科学平台。该方法将异常检测视为二分类问题，高效利用少量标注与大量未标注图像进行训练。通过用户界面实现主动学习：验证高置信度异常并修正误报。在银河系MNIST天文数据集和miniImageNet自然图像基准上，面对严重类别不平衡的情况，表现优异。从5到10个标注异常样本出发，平均达到0.96 AUROC（miniImageNet）和0.89 AUROC（GalaxyMNIST），对应AUPRC分别为0.82和0.77。经过三轮主动学习后，异常在最高评分前1%图像中精度达76%（miniImageNet）至94%（GalaxyMNIST）。与Astronomaly软件在'Galaxy Zoo - The Galaxy Challenge'数据集上的对比显示，平均AUROC达0.83，性能相当。结果表明，该方法在标签极度稀缺领域具有显著实用价值与可扩展性。
原文摘要 · Abstract (English)
Anomaly detection in large datasets is essential in astronomy and computer vision. However, due to a scarcity of labelled data, it is often infeasible to apply supervised methods to anomaly detection. We present AnomalyMatch, an anomaly detection framework combining the semi-supervised FixMatch algorithm using EfficientNet classifiers with active learning. AnomalyMatch is tailored for large-scale applications and integrated into the ESA Datalabs science platform. In this method, we treat anomaly detection as a binary classification problem and efficiently utilise limited labelled and abundant unlabelled images for training. We enable active learning via a user interface for verification of high-confidence anomalies and correction of false positives. Evaluations on the GalaxyMNIST astronomical dataset and the miniImageNet natural-image benchmark under severe class imbalance display strong performance. Starting from five to ten labelled anomalies, we achieve an average AUROC of 0.96 (miniImageNet) and 0.89 (GalaxyMNIST), with respective AUPRC of 0.82 and 0.77. After three active learning cycles, anomalies are ranked with 76% (miniImageNet) to 94% (GalaxyMNIST) precision in the top 1% of the highest-ranking images by score. We compare to the established Astronomaly software on selected 'odd' galaxies from the 'Galaxy Zoo- The Galaxy Challenge' dataset, achieving comparable performance with an average AUROC of 0.83. Our results underscore the exceptional utility and scalability of this approach for anomaly discovery, highlighting the value of specialised approaches for domains characterised by severe label scarcity
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