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.

翻译：摘要：近期深度度量学习中的诸多损失函数以对数和指数形式表达，其中边界和尺度是至关重要的超参数。由于每个数据类别具有内在特性，以往部分研究通过引入自适应边界来使嵌入空间更贴近真实数据分布。然而，尚无任何工作涉及自适应尺度。我们认为，在训练过程中边界和尺度均需具备自适应调节能力。本文提出一种名为自适应边界与自适应尺度（AdaMS）的方法，该方法将每个类别的边界和尺度超参数替换为可学习的自适应边界与自适应尺度参数。基于华尔街日报数据集的评估表明，本方法在词汇辨别任务中取得了优于现有方法的性能。