论文标题
CSI：通过对比度学习的新颖性检测在分布转移的实例上
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
论文作者
论文摘要
新颖性检测，即确定是否从训练分布外部绘制给定样品，对于可靠的机器学习至关重要。为此，已经有很多尝试学习一种非常适合新颖性检测和基于这种表示的分数的代表。在本文中，我们提出了一种简单而有效的方法，称为对比偏移的实例（CSI），这是受视觉表示对比度学习的最新成功的启发。具体而言，除了将给定样本与其他实例进行对比，如传统的对比学习方法外，我们的培训计划还将样本与分布偏移的增强对比。基于此，我们提出了一种针对拟议培训计划的新检测分数。我们的实验证明了我们方法在各种新颖性检测方案下的优越性，包括未标记的单级，未标记的多级和标签的多级设置以及各种图像基准数据集。代码和预培训模型可在https://github.com/alinlab/csi上找到。
Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have been many attempts at learning a representation well-suited for novelty detection and designing a score based on such representation. In this paper, we propose a simple, yet effective method named contrasting shifted instances (CSI), inspired by the recent success on contrastive learning of visual representations. Specifically, in addition to contrasting a given sample with other instances as in conventional contrastive learning methods, our training scheme contrasts the sample with distributionally-shifted augmentations of itself. Based on this, we propose a new detection score that is specific to the proposed training scheme. Our experiments demonstrate the superiority of our method under various novelty detection scenarios, including unlabeled one-class, unlabeled multi-class and labeled multi-class settings, with various image benchmark datasets. Code and pre-trained models are available at https://github.com/alinlab/CSI.
