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.

本文提出了一种多任务课程学习框架，旨在解决开放集合 SSL 中的未知样本问题，通过同时训练模型的正常分类能力和未知样本的检测能力，成功地消除了未知样本的影响。