考虑多粒度类相关性的对比式开放集识别方法

深度神经网络在分类任务上不断取得性能突破,但在测试中面对未知类样本时,会错误地给出一个已知类预测结果.开放集识别任务旨在解决该问题,要求模型不仅精确地分类已知类,同时对未知类样本进行准确判别.现有方法虽然取得了不错的效果,但由于未对开放集识别任务的影响因素进行分析,因而大多基于某种假设启发式地设计模型,难以保证对于实际场景的适应性.分析了现有方法的共性,通过设计一个新的决策变量实验,发现模型对已知类的表示学习能力是其中的一个关键影响因素.基于该结论,提出了一种基于模型表示学习能力增强的开放集识别方法.首先,由于对比式学习已展示出的强大表示学习能力以及开放集识别任务所包含的标签信息,引入了监督对比式学习方法,提高模型对已知类的建模能力;其次,考虑到类别间的相关性是在类别层次上的表示,且类别之间往往呈现分层结构关系,设计了一种多粒度类相关性的损失函数,通过在标签语义空间构建分层结构并度量多粒度类相关性的方式,约束模型学习不同已知类间的相关关系,进一步提高其表示学习能力;最后,在多个标准数据集上进行了实验验证,证明了所提出方法在开放集识别任务上的有效性.

Multi-granularity Inter-class Correlation Based Contrastive Learning for Open Set Recognition

In recent years, deep neural networks have continuously achieved breakthroughs in the classification task, but they will mistakenly give a wrong known class prediction when faced with unknown samples in the testing phase. The open set recognition is a possible way to solve the problem, which requires the model not only to classify the known classes, but also to distinguish the unknown samples accurately. Most of the existing methods are designed heuristically based on certain assumptions. Despite keeping the performance increasing, they have not analyzed the key factors that affect the task. In this paper, we analyze the commonalities of existing methods by designing a new decision variable experiment and find that the ability of model to learn representations of known classes is an important factor. Then an open set recognition method is proposed based on model representation learning ability enhancement. Firstly, due to the powerful representation learning capabilities demonstrated by the contrastive learning and the label information contained in the

open set recognition task, supervised contrastive learning is introduced to improve the modeling ability of the model to known classes. Secondly, considering that the correlation among the categories is the representation learning at the category level, and the hierarchical structure relationship among the categories is often presented, a multi-granularity inter-class correlation loss is designed by building the hierarchical structure in the label semantic space and measuring the multi-granularity inter-class correlation. The multi-granularity inter-class correlation loss constrains the model to learn the correlation among different known classes to further improve the representation learning ability of model. Finally, experimental results on multiple standard datasets verify the effectiveness of the proposed method on open set recognition tasks.