Zero-sample image classification model based on generative adversarial network and method thereof

Abstract

The invention relates to a zero-sample image classification model based on a generative adversarial network, and the model comprises a generative adversarial network module which is used for obtainingvisual error information; a visual feature extraction network processing module used for obtaining a one-dimensional visual feature vector of an image; an attribute semantic conversion network moduleused for mapping a low-dimensional attribute semantic vector to a high-dimensional feature vector with the same dimension as the visual feature vector by using two linear activation layers; a vision-attribute semantic connection network used for realizing fusion of a vision feature vector and an attribute semantic feature vector; and a score classification result and reward output module usedfor classifying the types with the labels seen by adopting cross entropy loss, and reward output is used for punishing the unseen data without the labels and punishing the prediction result with the highest possibility of the seen types and the unseen types in the data without the labels. According to the invention, the problem of image category label missing can be effectively solved.

Description

technical field [0001] The invention relates to a zero-sample image classification model, in particular to a zero-sample image classification model based on a generative confrontation network and a method thereof. Background technique [0002] Currently, in the process of image classification, if you want to accurately classify images, you need to inform the model of the image labels of each category. However, the number of image categories is often very large, and new categories may be added from time to time. If each category label is manually labeled each time, the workload will be extremely huge. In this process, some categories have only a few or no training sample labels, and the entire category without training labels belongs to zero samples. Such zero samples cannot be effectively constructed by using traditional machine learning methods to construct classifiers. The purpose of zero-shot learning image classification is to solve the problem of missing the entire cat...

AI Technical Summary

Problems solved by technology

However, the number of image categories is often very large, and new categories may be added from time to time. If each category label is manually labeled each time, the workload will be extremely huge.

In this process, some categories have only a few or no training sample labels, and the entire category without training labels belongs to zero samples. Such zero samples cannot be effectively constructed by using traditional machine learning methods.

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