In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims
at inferring novel object categories in an unlabeled set by leveraging from
prior knowledge of a labeled set containing different, but related classes.
Existing approaches tackle this problem by considering multiple objective
functions, usually involving specialized loss terms for the labeled and the
unlabeled samples respectively, and often requiring auxiliary regularization
terms. In this paper, we depart from this traditional scheme and introduce a
UNified Objective function (UNO) for discovering novel classes, with the
explicit purpose of favoring synergy between supervised and unsupervised
learning. Using a multi-view self-labeling strategy, we generate pseudo-labels
that can be treated homogeneously with ground truth labels. This leads to a
single classification objective operating on both known and unknown classes.
Despite its simplicity, UNO outperforms the state of the art by a significant
margin on several benchmarks (~+10% on CIFAR-100 and +8% on ImageNet). The
project page is available at: this https URL

本文旨在研究通过使用已标记类别的先前知识来推断未标记集合中的新物体类别的新类发现问题。我们引入了一个称为 UNO 的统一目标函数，以利用多视角自标记策略生成伪标签以了解已知和未知类别，从而克服了现有方法中的问题，并在几个基准测试中显着优于现有技术。