论文标题
开放设定半监督学习的多任务课程框架
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
论文作者
论文摘要
半监督学习（SSL）已提出，仅当只有有限的标记数据可用时，就可以利用未标记的数据来培训强大的模型。尽管现有的SSL方法假设标记和未标记的数据中的样本共享样本的类别，但我们解决了一个更复杂的新颖方案，称为开放式SSL，其中未标记的数据中包含了分布式（OOD）样本。我们提出了一个多任务课程学习框架，而不是分别训练OOD检测器和SSL。首先，为了检测未标记数据中的OOD样品，我们估计了属于OOD的样品的概率。我们使用联合优化框架，该框架更新网络参数，并且交替地分数。同时，为了在分布分类（ID）数据的分类中实现高性能，我们在具有较小的OOD分数的未标记数据中选择ID样本，并将这些数据与标记的数据一起用于训练深层神经网络以半手不足的方式对ID进行分类。我们进行了几项实验，我们的方法通过成功消除了OOD样品的效果来实现最新的结果。
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. While existing SSL methods assume that samples in the labeled and unlabeled data share the classes of their samples, we address a more complex novel scenario named open-set SSL, where out-of-distribution (OOD) samples are contained in unlabeled data. Instead of training an OOD detector and SSL separately, we propose a multi-task curriculum learning framework. First, to detect the OOD samples in unlabeled data, we estimate the probability of the sample belonging to OOD. We use a joint optimization framework, which updates the network parameters and the OOD score alternately. Simultaneously, to achieve high performance on the classification of in-distribution (ID) data, we select ID samples in unlabeled data having small OOD scores, and use these data with labeled data for training the deep neural networks to classify ID samples in a semi-supervised manner. We conduct several experiments, and our method achieves state-of-the-art results by successfully eliminating the effect of OOD samples.
