Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

FOS: Computer and information sciences Computer Science - Machine Learning 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning 4. Education 0202 electrical engineering, electronic engineering, information engineering Machine Learning (stat.ML) 02 engineering and technology 3. Good health Machine Learning (cs.LG)
DOI: 10.48550/arxiv.2006.11280 Publication Date: 2020-01-01
ABSTRACT
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples. While current state-of-the-art methods employ importance reweighting to design various risk estimators, they ignored the learning capability of the model itself, which could have provided reliable supervision. This motivates us to propose a novel Self-PU learning framework, which seamlessly integrates PU learning and self-training. Self-PU highlights three "self"-oriented building blocks: a self-paced training algorithm that adaptively discovers and augments confident positive/negative examples as the training proceeds; a self-calibrated instance-aware loss; and a self-distillation scheme that introduces teacher-students learning as an effective regularization for PU learning. We demonstrate the state-of-the-art performance of Self-PU on common PU learning benchmarks (MNIST and CIFAR-10), which compare favorably against the latest competitors. Moreover, we study a real-world application of PU learning, i.e., classifying brain images of Alzheimer's Disease. Self-PU obtains significantly improved results on the renowned Alzheimer's Disease Neuroimaging Initiative (ADNI) database over existing methods. The code is publicly available at: https://github.com/TAMU-VITA/Self-PU.<br/>ICML 2020 accepted<br/>
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