论文标题
标签繁殖自适应共振理论，用于半监督的连续学习
Label Propagation Adaptive Resonance Theory for Semi-supervised Continuous Learning
论文作者
论文摘要
半监督学习和持续学习是人类智能的基本范式。为了处理很少给出标签并且访问相同数据的机会的现实问题，有限公司有必要以连接的方式应用这两个范式。在本文中，我们提出了标签传播自适应共振理论（LPART），以进行半监视的持续学习。 LPART使用在线标签传播机制来执行分类，并随着观察到的数据累积而逐渐提高其准确性。我们通过调整标记和未标记的数据的比率来评估Visual（MNIST，SVHN，CIFAR-10）和音频（NSYNTH）数据集的建议模型。当使用标记和未标记数据时，精度要高得多，这表明LPART在数据标签稀缺的环境中具有显着优势。
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these two paradigms in a joined fashion. In this paper, we propose Label Propagation Adaptive Resonance Theory (LPART) for semi-supervised continuous learning. LPART uses an online label propagation mechanism to perform classification and gradually improves its accuracy as the observed data accumulates. We evaluated the proposed model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets by adjusting the ratio of the labeled and unlabeled data. The accuracies are much higher when both labeled and unlabeled data are used, demonstrating the significant advantage of LPART in environments where the data labels are scarce.
