论文标题
亚当斯：具有自适应余量和自适应量表的深度度量学习
AdaMS: Deep Metric Learning with Adaptive Margin and Adaptive Scale for Acoustic Word Discrimination
论文作者
论文摘要
深度度量学习中的许多近期损失功能都以对数和指数形式表达，它们涉及边缘和规模作为必不可少的超参数。由于每个数据类具有一个内在的特征，因此以前的几项工作试图通过引入自适应边缘来学习接近实际分布的嵌入空间。但是，根本没有自适应量表的工作。我们认为，在培训期间，边际和规模都应适应性调节。在本文中，我们提出了一种称为自适应边缘和尺度（ADAMS）的方法，其中的超级参数和比例尺被每个班级的自适应边缘和自适应尺度的可学习参数取代。我们的方法在Wall Street Journal数据集中进行了评估，我们在单词歧视任务方面取得了优于表现的结果。
Many recent loss functions in deep metric learning are expressed with logarithmic and exponential forms, and they involve margin and scale as essential hyper-parameters. Since each data class has an intrinsic characteristic, several previous works have tried to learn embedding space close to the real distribution by introducing adaptive margins. However, there was no work on adaptive scales at all. We argue that both margin and scale should be adaptively adjustable during the training. In this paper, we propose a method called Adaptive Margin and Scale (AdaMS), where hyper-parameters of margin and scale are replaced with learnable parameters of adaptive margins and adaptive scales for each class. Our method is evaluated on Wall Street Journal dataset, and we achieve outperforming results for word discrimination tasks.
