A well trained and generalized deep neural network (DNN) should be robust to both seen and unseen classes. However, the performance of most of the existing supervised DNN algorithms degrade for classes which are unseen in the training set. To learn a discriminative classifier which yields good performance in Zero-Shot Learning (ZSL) settings, we propose to generate an Over-Complete Distribution (OCD) using Conditional Variational Autoencoder (CVAE) of both seen and unseen classes. In order to enforce the separability between classes and reduce the class scatter, we propose the use of Online Batch Triplet Loss (OBTL) and Center Loss (CL) on the generated OCD. The effectiveness of the framework is evaluated using both Zero-Shot Learning and Generalized Zero-Shot Learning protocols on three publicly available benchmark databases, SUN, CUB and AWA2. The results show that generating over-complete distributions and enforcing the classifier to learn a transform function from overlapping to non-overlapping distributions can improve the performance on both seen and unseen classes.

该研究提出了一种通过使用CVAE生成超完备分布并在其上使用OBTL和CL来改进DNN在ZSL设置下的性能的框架，并在SUN、CUB和AWA2三个公开基准数据库上进行了评估，结果表明生成超完备分布并迫使分类器从重叠到非重叠分布的转换函数可以提高对已知和未知类别的性能。

通过过完备分布实现广义零样本学习