Semi-supervised learning (SSL) is one of the dominant approaches to address
the annotation bottleneck of supervised learning. Recent SSL methods can
effectively leverage a large repository of unlabeled data to improve
performance while relying on a small set of labeled data. One common assumption
in most SSL methods is that the labeled and unlabeled data are from the same
data distribution. However, this is hardly the case in many real-world
scenarios, which limits their applicability. In this work, instead, we attempt
to solve the challenging open-world SSL problem that does not make such an
assumption. In the open-world SSL problem, the objective is to recognize
samples of known classes, and simultaneously detect and cluster samples
belonging to novel classes present in unlabeled data. This work introduces
OpenLDN that utilizes a pairwise similarity loss to discover novel classes.
Using a bi-level optimization rule this pairwise similarity loss exploits the
information available in the labeled set to implicitly cluster novel class
samples, while simultaneously recognizing samples from known classes. After
discovering novel classes, OpenLDN transforms the open-world SSL problem into a
standard SSL problem to achieve additional performance gains using existing SSL
methods. Our extensive experiments demonstrate that OpenLDN outperforms the
current state-of-the-art methods on multiple popular classification benchmarks
while providing a better accuracy/training time trade-off.

本文介绍了一种名为 OpenLDN 的方法，该方法针对半监督学习中存在的已标记数据和未标记数据来自于相同数据分布的困难，解决了开放世界半监督学习的问题，通过使用成对相似性损失来识别已知类别的样本，同时探测和聚类未标记数据中属于新类别的样本。实验结果表明，OpenLDN 在多个常用分类基准测试中都优于现有最佳方法，具有更好的准确性 / 训练时间折衷。