基于低秩堆栈式语义自编码器的零样本学习.

In the field of image classification, existing methods such as deep learning require a large number of annotated samples for training and are unable Lo identify classes that do not appear in the training phase. Zero-shot learning tasks can effectively alleviate such problems. This study proposed a new zero-shot learning method, namely low-rank stacked semantic auto-encoder (LSSAE) based on slacked auto-encoder and low-rank embedding. The model was based on an encoding-decoding mechanism where the encoder learned a projection function with a low-rank structure for concatenating the visual feature space, the semantic space and the labels. It reconstructed the original visual features in the decoding stage. And the low-rank embedding enabled the learned model to share the semantic information of the seen classes when anticipating the unseen classes for better classification. Experiments were conducted on five common datasets in this study, and the results show that the proposed LSSAE outperforms existing zero-shot learning methods which is an effective zero-shot learning method. [ABSTRACT FROM AUTHOR]