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：由于每个数据类具有内在特征,前几部著作试图通过引入适应性边距来学习将空间嵌入到接近实际分布的空间中。然而,根本没有关于适应性尺度的工作。我们主张,在培训期间,边距和比例应可适应性地调整。在本文中,我们提出了一种称为适应性边距和比例(AdaMS)的方法,即以适应性边距和比例的可学习参数取代边距和比例的超大参数,以适应性边距和每个等级的适应性尺度。我们的方法在《华尔街日报》数据集中进行了评估,我们在文字歧视任务上取得了优异的结果。