Dilated-convolution method based on deep convolutional adversarial network model

Abstract

The invention discloses a dilated-convolution method based on a deep convolutional adversarial network model, and belongs to the fields of deep learning and neural networks. The dilated-convolution method includes the following steps: S1, constructing an original generative adversarial network (GAN) model; S2, constructing deep convolutional neural networks to use the same as a generator and a discriminator; S3, initializing random noises, and inputting the same into the generator; S4, utilizing dilated convolution to carry out convolution operations on images in the neural network; and S5, inputting a loss function value, which is obtained by the dilated-convolution operations, into the generator for subsequent training. According to the dilated-convolution method based on the deep convolutional adversarial network model constructed in the invention, a convolution manner of the discriminator and the generator after receiving a picture is changed, the discriminator and the generator are enabled to learn features of the image in a larger range, and thus robustness of the entire network training model can be improved.

Description

technical field [0001] The invention relates to the technical field of deep learning neural networks, in particular to an atrous convolution method based on a deep convolutional 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 confrontational network model, the discriminator often operates through the original convolution kernel, analyzes the features of the image generated by the generator, and learns a new feature. It often needs to learn an old feature. Only after it is over. In this case, the range of f...

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In this case, the range of features learned by the g

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