论文标题
基于距离比率的公制表述
Distance-Ratio-Based Formulation for Metric Learning
论文作者
论文摘要
在公制学习中，目标是学习一个嵌入，以便具有同一类的数据点彼此接近，并且具有不同类别的数据点相距甚远。我们提出了一种基于距离的（DR）公式用于公制学习。就像用于公制学习的基于软马克斯的公式一样，它模型$ p（y = c | x'）$，这是查询点$ x'$属于$ c $的概率。 DR公式具有两个有用的属性。首先，相应的损失不受嵌入的比例变化的影响。其次，它在代表类的点上输出最佳（最大或最小）分类置信度得分。为了证明我们的配方的有效性，我们使用基于软马克斯和Mini-Imagenet数据集的基于软max和DR制剂进行了很少的射击分类实验。结果表明，DR公式通常比基于SoftMax的配方更快，更稳定的度量学习。结果，使用DR公式可提高或可比的概括性能。
In metric learning, the goal is to learn an embedding so that data points with the same class are close to each other and data points with different classes are far apart. We propose a distance-ratio-based (DR) formulation for metric learning. Like softmax-based formulation for metric learning, it models $p(y=c|x')$, which is a probability that a query point $x'$ belongs to a class $c$. The DR formulation has two useful properties. First, the corresponding loss is not affected by scale changes of an embedding. Second, it outputs the optimal (maximum or minimum) classification confidence scores on representing points for classes. To demonstrate the effectiveness of our formulation, we conduct few-shot classification experiments using softmax-based and DR formulations on CUB and mini-ImageNet datasets. The results show that DR formulation generally enables faster and more stable metric learning than the softmax-based formulation. As a result, using DR formulation achieves improved or comparable generalization performances.
