Visual recognition tasks are often limited to dealing with a small subset of
classes simply because the labels for the remaining classes are unavailable. We
are interested in identifying novel concepts in a dataset through
representation learning based on both labeled and unlabeled examples, and
extending the horizon of recognition to both known and novel classes. To
address this challenging task, we propose a combinatorial learning approach,
which naturally clusters the examples in unseen classes using the compositional
knowledge given by multiple supervised meta-classifiers on heterogeneous label
spaces. The representations given by the combinatorial embedding are made more
robust by unsupervised pairwise relation learning. The proposed algorithm
discovers novel concepts via a joint optimization for enhancing the
discrimitiveness of unseen classes as well as learning the representations of
known classes generalizable to novel ones. Our extensive experiments
demonstrate remarkable performance gains by the proposed approach on public
datasets for image retrieval and image categorization with novel class
discovery.

通过表示学习，联合优化未知类的区分度和已知类的可泛化性，使用组合学习方法基于多个异构标签空间上的监督元分类器的复合知识自然地对未知类中的示例进行聚类，并通过无监督成对关系学习使待检测类别的特征更具有鲁棒性，实现对公共数据集的图像检索和图像分类的性能显著提升，并发现新概念。