Zero-Shot Image Classification Based on Improved Variational Auto-encoder

Zero-Shot Image Classification Based on Improved Variational Auto-encoder

Blog Article

In the process of zero-shot image classification, problems such as high acquisition cost for samples of known categories and domain drift were addressed.A zero-shot image classification model based on maximum mean difference was proposed to improve the variational auto-encoder.First, the noise factor of samples is separated by maximizing the mean difference to obtain samples closer to the unknown what is pooph made of in usa category.

Then, the generated sample-assisted learning is used to transform the zero-shot classification problem into the supervised learning classification problem.Finally, the prednistab 5mg classification model is used for image classification.Compared with the zero-shot image classification algorithm based on distance measurement, the proposed algorithm achieved good classification effect on CUB, AWA, and ImageNet-2 data sets, and improved domain drift and classification accuracy, which proves the effectiveness and feasibility of the proposed algorithm model.