Multi-scale convolution kernel method based on text-image generative adversarial network model

Abstract

The invention discloses a multi-scale convolution kernel method based on a text-image generative adversarial network model. The method comprises the following steps that: S1: constructing the text-image generative adversarial network model; S2: utilizing a deep convolutional neural network to serve as the functions of a generator and a discriminator; S3: after a text is coded, combining with random noise, and inputting the combined text and random noise into the generator; S4: in the text-image generative adversarial network model, utilizing multi-scale convolution to carry out a convolution operation on an image; and S5: inputting a loss function obtained by the multi-scale convolution operation into the generator for subsequent training. By use of the text-image generative adversarial network model constructed by the method, a convolution way generated after the generator and the discriminator receive pictures is changed through the multi-scale convolution, an original operation thatonly one convolution kernel is used by aiming at a single-layer image channel is changed into a situation that a plurality of convolution kernels are simultaneously adopted, so that the whole networkcan learn more characteristics when the single-layer image channel is convoluted, and network training efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of deep learning neural networks, in particular to a multi-scale convolution kernel method based on a text-image generation confrontation network model. Background technique [0002] Generative Adversarial Network (GAN for short) is a deep learning framework proposed by Goodfellow in 2014. It is based on the idea of ​​"game theory" and constructs two models, the generator and the discriminator. The former The image is generated by inputting (0, 1) uniform noise or Gaussian random noise, which discriminates the input image to determine whether it is an image from the dataset or an image produced by the generator. [0003] In the traditional adversarial network model, the discriminator and the generator use the convolution method after receiving the picture. Originally, only one convolution kernel is used for the single-layer image channel, which makes the network learn features slowly during the training proc...

AI Technical Summary

Problems solved by technology

[0003] In the traditional adversarial network model, the discriminator and the generator use the convolution method after receiving the picture. Originally, only one convolution kernel is used for the single-layer image channel, which makes the network learn features slowly during the training process. For Each layer of image channels can only learn one feature

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