论文标题
一个针对监督深度度量学习量身定制的新相似空间
A New Similarity Space Tailored for Supervised Deep Metric Learning
论文作者
论文摘要
我们提出了一种新颖的深度度量学习方法。与该领域的许多作品不同，我们定义了通过自动编码器获得的新型潜在空间。新空间，即S空间，分为不同的区域，这些区域描述了对象对相似/不同的位置。我们找到制造商来识别这些地区。我们通过基于内核的t-student分布来估计对象之间的相似性，以测量标记的距离和新的数据表示。在我们的方法中，我们同时估计标记在S空间中的位置，并表示同一空间中的对象。此外，我们提出了一种新的正则化功能，以避免类似标记以完全崩溃。我们介绍了我们的建议可以代表复杂空间的证据，例如，当类似对象的组位于分离区域时。我们将我们的建议与在28个现实世界中异质数据集上的9种不同距离度量学习方法（其中四种基于深度学习）进行了比较。根据所使用的四个定量指标，我们的方法克服了文献中的所有九种策略。
We propose a novel deep metric learning method. Differently from many works on this area, we defined a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions that describe the positions where pairs of objects are similar/dissimilar. We locate makers to identify these regions. We estimate the similarities between objects through a kernel-based t-student distribution to measure the markers' distance and the new data representation. In our approach, we simultaneously estimate the markers' position in the S-space and represent the objects in the same space. Moreover, we propose a new regularization function to avoid similar markers to collapse altogether. We present evidences that our proposal can represent complex spaces, for instance, when groups of similar objects are located in disjoint regions. We compare our proposal to 9 different distance metric learning approaches (four of them are based on deep-learning) on 28 real-world heterogeneous datasets. According to the four quantitative metrics used, our method overcomes all the nine strategies from the literature.
