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

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

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In the process of zero-shot image classification, problems such as high acquisition cost for samples of known categories and domain 2000 corvette radiator 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 category.Then, the generated sample-assisted jake wire tighteners learning is used to transform the zero-shot classification problem into the supervised learning classification problem.Finally, the 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.