论文标题
对象检测中的OOD的对比度学习
Contrastive Learning for OOD in Object detection
论文作者
论文摘要
对比学习通常应用于自学的学习，并且已被证明超过了传统方法，例如三胞胎损失和n对损失。但是，大批量和记忆库的要求使训练变得困难和缓慢。最近，已经开发出有监督的对比方法来克服这些问题。他们更多地专注于分别或在各个班级之间为每个班级学习一个良好的表示。在这项工作中，我们尝试使用用户定义的排名来基于相似性对类进行排名，以了解所有类之间的有效表示。我们观察到将人类偏见纳入学习过程如何可以改善参数空间中的学习表征。我们表明，我们的结果可与受监督的对比度学习进行图像分类和对象检测相媲美，并讨论其在OOD检测中的缺点
Contrastive learning is commonly applied to self-supervised learning, and has been shown to outperform traditional approaches such as the triplet loss and N-pair loss. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. Recently, Supervised Contrasative approaches have been developed to overcome these problems. They focus more on learning a good representation for each class individually, or between a cluster of classes. In this work we attempt to rank classes based on similarity using a user-defined ranking, to learn an efficient representation between all classes. We observe how incorporating human bias into the learning process could improve learning representations in the parameter space. We show that our results are comparable to Supervised Contrastive Learning for image classification and object detection, and discuss it's shortcomings in OOD Detection
