In this paper, we address a complex but practical scenario in semi-supervised
learning (SSL) named open-set SSL, where unlabeled data contain both
in-distribution (ID) and out-of-distribution (OOD) samples. Unlike previous
methods that only consider ID samples to be useful and aim to filter out OOD
ones completely during training, we argue that the exploration and exploitation
of both ID and OOD samples can benefit SSL. To support our claim, i) we propose
a prototype-based clustering and identification algorithm that explores the
inherent similarity and difference among samples at feature level and
effectively cluster them around several predefined ID and OOD prototypes,
thereby enhancing feature learning and facilitating ID/OOD identification; ii)
we propose an importance-based sampling method that exploits the difference in
importance of each ID and OOD sample to SSL, thereby reducing the sampling bias
and improving the training. Our proposed method achieves state-of-the-art in
several challenging benchmarks, and improves upon existing SSL methods even
when ID samples are totally absent in unlabeled data.

本文探讨半监督学习中的开放集问题，提出了基于原型的聚类和识别算法以及基于重要性采样的方法，针对多项挑战性基准测试取得最优成果。

未标记数据的开放集半监督学习中的探索和利用

Exploration and Exploitation of Unlabeled Data for Open-Set  Semi-Supervised Learning

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。