In this paper, we address Novel Class Discovery (NCD), the task of unveiling
new classes in a set of unlabeled samples given a labeled dataset with known
classes. We exploit the peculiarities of NCD to build a new framework, named
Neighborhood Contrastive Learning (NCL), to learn discriminative
representations that are important to clustering performance. Our contribution
is twofold. First, we find that a feature extractor trained on the labeled set
generates representations in which a generic query sample and its neighbors are
likely to share the same class. We exploit this observation to retrieve and
aggregate pseudo-positive pairs with contrastive learning, thus encouraging the
model to learn more discriminative representations. Second, we notice that most
of the instances are easily discriminated by the network, contributing less to
the contrastive loss. To overcome this issue, we propose to generate hard
negatives by mixing labeled and unlabeled samples in the feature space. We
experimentally demonstrate that these two ingredients significantly contribute
to clustering performance and lead our model to outperform state-of-the-art
methods by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8%
on ImageNet).

本研究提出了一种名为 NCL 的新框架，旨在学习具有判别性的表示，从而创建一个用于聚类性能的新类别发现（NCD）任务，其中，利用特征提取器生成的表示来检索和聚合伪正对，虽然大多数实例很容易通过网络进行区分，但我们通过在特征空间中混合标记和未标记的样本来生成难以区分的样本。实验证明，这两个组成部分显著提高了聚类性能，并使我们的模型在 CIFAR-100 和 ImageNet 上的聚类准确性提高 13％和 8％，远远超过现有方法。