OpenMix：在开放世界中恢复已知知识以发现新的视觉类别
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
摘要：
在本文中，我们解决的问题，发现新的类在未标记的视觉数据标记的数据从不相交的类。现有的方法通常首先用标记数据预训练模型，然后通过无监督聚类在未标记数据中识别新的类。然而，在第二步中，提供基本知识的标记数据往往未被充分利用。挑战在于标记和未标记的示例来自非重叠的类，这使得很难在它们之间建立学习关系。在这项工作中，我们引入OpenMix来混合来自开放集合的未标记示例和来自已知类的标记示例，其中它们的非重叠标签和伪标签同时混合到联合标签分布中。OpenMix以两种方式动态地组合示例。首先，我们通过将标记的示例与未标记的示例相结合来生成混合训练图像。由于新类发现中的先验知识是唯一的，因此生成的伪标签比原始的未标记预测更可信。因此，OpenMix有助于防止模型在未标记的样本上过度拟合，这些样本可能被分配了错误的伪标签。第二，第一种方法鼓励具有高类概率的未标记示例具有相当的准确性。我们将这些例子作为可靠的锚点引入，并将它们与未标记的样本进一步整合。这使我们能够在未标记的示例中生成更多的组合，并利用新类之间更精细的对象关系。在三个分类数据集上的实验证明了所提出的OpenMix的有效性，这是优于国家的最先进的方法，在新的类发现。
英文摘要：
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a model with labeled data, and then identify new classes in unlabeled data via unsupervised clustering. However, the labeled data that provide essential knowledge are often underexplored in the second step. The challenge is that the labeled and unlabeled examples are from non-overlapping classes, which makes it difficult to build the learning relationship between them. In this work, we introduce OpenMix to mix the unlabeled examples from an open set and the labeled examples from known classes, where their non-overlapping labels and pseudo-labels are simultaneously mixed into a joint label distribution. OpenMix dynamically compounds examples in two ways. First, we produce mixed training images by incorporating labeled examples with unlabeled examples. With the benefits of unique prior knowledge in novel class discovery, the generated pseudo-labels will be more credible than the original unlabeled predictions. As a result, OpenMix helps to prevent the model from overfitting on unlabeled samples that may be assigned with wrong pseudo-labels. Second, the first way encourages the unlabeled examples with high class-probabilities to have considerable accuracy. We introduce these examples as reliable anchors and further integrate them with unlabeled samples. This enables us to generate more combinations in unlabeled examples and exploit finer object relations among the new classes. Experiments on three classification datasets demonstrate the effectiveness of the proposed OpenMix, which is superior to state-of-the-art methods in novel class discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
