Zero-sample image classification method based on regression variation auto-encoder

Abstract

The invention discloses a zero-sample image classification method based on a regression variation auto-encoder. The invention relates to a method for identifying graphs by using electronic equipment.A variational auto-encoder is reconstructed by training, aligning and crossing; calculating regression network loss, the global loss L of the whole model network is calculated, the classifier is trained, the classification accuracy is calculated, zero-sample image classification based on the regression variation auto-encoder is completed, and the defects that in the prior art of generalized zero-sample classification, training samples are deficient, generated samples lack semantics, and a generative adversarial network is prone to collapse are overcome.

Description

technical field [0001] The technical solution of the present invention relates to a method for identifying graphics using electronic equipment, specifically a zero-sample image classification method based on a regression variational autoencoder. Background technique [0002] With the continuous development of deep learning in the field of computer vision, the demand for training data is also expanding. However, in reality, it is difficult to obtain a large amount of labeled data for supervised training, so the study of zero-shot image classification problems is particularly important. The zero-sample image classification problem is to establish the connection between known class images and unknown class images through knowledge transfer, so as to realize the requirement that the model trained with known class images can classify images of unknown classes, so as to achieve zero-sample image classification. Sample image classification purposes. For example, for bears, zebras...

AI Technical Summary

Problems solved by technology

[0012] When solving the generalized zero-sample image classification problem, a part of the known class data set in the database is used as a training set, and the other part and an unknown class data set are used as a test set, and the data used include known class image features v seen Data and known class semantic features c seen data, and unknown class image features v unseen Data and Unknown Class Semantic Features c unseen Data, all data are from the selected data disclosed in the document "Zero-Shot Learning-A Comprehensive Evaluation of the Good, the Bad and the Ugly", all image features are from the well-known residual network ResNet-101 of 2048 Dimensional final pooling layer, the data used is manually marked with attribute information as semantic features to complete data preprocessing;

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