利用类相似性进行机器学习的置信标签和投影损失函数
Exploiting Class Similarity for Machine Learning with Confidence Labels and Projective Loss Functions
摘要：
用于机器学习的类标签是相互关联的，某些类标签比其他类标签更相似（例如，猫和狗的图像比猫和汽车的图像更相似）。类之间的这种相似性通常是由于模型之间的混淆而导致模型性能差的原因。当前的标记技术不能明确地捕获这样的相似性信息。在本文中，我们而是利用类之间的相似性捕获的相似性信息与我们的新的信心标签。置信度标签是概率标签，表示类之间的相似性或易混淆性的可能性。通常，即使在训练模型以区分特征空间中的类别之后，相似类别的潜在空间仍然保持聚类。我们认为这种类型的聚类作为有价值的信息，并利用我们的新的投影损失函数。我们的投影损失函数被设计为与置信度标签一起工作，并且能够放松对混淆相似类的错误的损失惩罚。我们使用我们的方法来训练带有噪声标签的神经网络，因为我们相信噪声标签部分是由于类相似性引起的混淆。与使用标准损失函数相比，我们显示出改进的性能。我们使用CIFAR-10数据集进行了详细的分析，并展示了我们提出的方法对较大数据集的适用性，例如ImageNet和Food-101 N。
英文摘要：
Class labels used for machine learning are relatable to each other, with certain class labels being more similar to each other than others (e.g. images of cats and dogs are more similar to each other than those of cats and cars). Such similarity among classes is often the cause of poor model performance due to the models confusing between them. Current labeling techniques fail to explicitly capture such similarity information. In this paper, we instead exploit the similarity between classes by capturing the similarity information with our novel confidence labels. Confidence labels are probabilistic labels denoting the likelihood of similarity, or confusability, between the classes. Often even after models are trained to differentiate between classes in the feature space, the similar classes' latent space still remains clustered. We view this type of clustering as valuable information and exploit it with our novel projective loss functions. Our projective loss functions are designed to work with confidence labels with an ability to relax the loss penalty for errors that confuse similar classes. We use our approach to train neural networks with noisy labels, as we believe noisy labels are partly a result of confusability arising from class similarity. We show improved performance compared to the use of standard loss functions. We conduct a detailed analysis using the CIFAR-10 dataset and show our proposed methods' applicability to larger datasets, such as ImageNet and Food-101N.
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。
