Generalized zero-shot learning (GZSL) is one of the most realistic but challenging problems due to the partiality of the classifier to supervised classes, especially under the class-inductive instance-inductive (CIII) training setting, where testing data are not available. Instance-borrowing methods and synthesizing methods solve it to some extent with the help of testing semantics, but therefore neither can be used under CIII. Besides, the latter require the training process of a classifier after generating examples. In contrast, a novel non-transductive regularization under CIII called Semantic Borrowing (SB) for improving GZSL methods with compatibility metric learning is proposed in this paper, which not only can be used for training linear models, but also nonlinear ones such as artificial neural networks. This regularization item in the loss function borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unknown classes would not be available for training while this approach does NOT need it. Extensive experiments on GZSL benchmark datasets show that SB can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification, surpassing inductive GZSL state of the arts.
翻译:通用零光学习(GZSL)是最现实但具有挑战性的问题之一,因为分类者偏向于监管的班级,特别是在没有测试数据的班级感性试感(CIII)培训设置下,没有测试数据。 例举方法和合成方法在某种程度上通过测试语义学解决了这个问题,但因此在CIII下也不能使用。 此外,CZSL要求分类者在生成实例后进行分类者的培训过程更为精确。相比之下,本文建议采用新的非转化性正规化(CIII,称为SB)来改进GZSL方法,使之与兼容性度衡量学习(CIII)相比,这不仅可用于培训线性模型,而且还可用于人工神经网络等非线性模型。 损失函数中的这一正规化项目在培训设置中借用了类似的语义学,因此,分类者可以在培训期间更准确地模拟零光谱和监管班级之间的关系。 实际上,未知班级的语义学术语学信息将无法用于培训,而这一方法不仅可用于培训线性模型,而且还可用于培训线性模型,因此需要将GSLSL的高级等级升级为SLA级的升级。