The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classification for the methods maximizing the margin in Euclidean spaces whereas the Cosine similarity distance is used during the testing stage for the methods maximizing margin in the angular spaces. This paper introduces a novel classification loss that maximizes the margin in both the Euclidean and angular spaces at the same time. This way, the Euclidean and Cosine distances will produce similar and consistent results and complement each other, which will in turn improve the accuracies. The proposed loss function enforces the samples of classes to cluster around the centers that represent them. The centers approximating classes are chosen from the boundary of a hypersphere, and the pairwise distances between class centers are always equivalent. This restriction corresponds to choosing centers from the vertices of a regular simplex. There is not any hyperparameter that must be set by the user in the proposed loss function, therefore the use of the proposed method is extremely easy for classical classification problems. Moreover, since the class samples are compactly clustered around their corresponding means, the proposed classifier is also very suitable for open set recognition problems where test samples can come from the unknown classes that are not seen in the training phase. Experimental studies show that the proposed method achieves the state-of-the-art accuracies on open set recognition despite its simplicity.
翻译:在深神经网络分类器中使用的分类损失功能可以分为两类,其依据是最大限度地增加欧克利底或角空间的差值。在对欧克利底空间差值最大化的方法进行分类时,将样本矢量之间的偏差用于欧洲克利底空间差值最大化方法的分类,而在对角空间差值最大化方法的测试阶段,将科辛相似距离用于科辛相近的分类功能。本文引入一种新的分类损失,使欧克利底和角空间的差值在同一时间最大化。这样,欧克利底和科辛距离将产生相似和一致的结果,并相互补充,从而反过来改善偏差值。拟议的损失函数在分类中强制使用类别样本样本样本样本的样本集集集集,因此,拟议的分类组群集分类法的分类法比较不易理解。提议的分类法类集的分类法比较不易理解。提议的分类法类中,提议的分类法类分类法的分类法类的分类方法比较不易识别。因此,提议的分类法类中的拟议分类法的分类法类解的分类法方法比较难解,因此,提议的分类法的分类法的分类法类的分类法方法的分类法性方法比较难于其分类法的分类法的分类法系的分类法则比较难。因此,因此,提议的分类法的分类法的分类法的分类法的分类法的分类法的分类法的分类法的分类法的分类法性方法的分类法是其的分类法法则在较难。在提议的分类法方法的分类法方法的分类法则在较难。