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Face image generation method based on gans

A face image and image technology, applied in the computer field, can solve the problems of many parameter update iterations, easy loss of small patch areas or detailed textures, complex optimization goals, etc., to achieve reasonable clarity, avoid gradient disappearance, reduce image effect

Active Publication Date: 2021-05-11
SOUTHWEST JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

BEGANs improves the discriminator with the help of encoding and decoding ideas, and then optimizes the W distance, but the optimization goal is more complicated, and small patch areas or detailed textures are prone to be lost in the generated images.
In addition, in order to achieve a better training effect, they need more parameter update iterations.

Method used

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  • Face image generation method based on gans
  • Face image generation method based on gans
  • Face image generation method based on gans

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] Please refer to figure 1 , figure 2 and Figure 4 , the embodiment of the present invention provides a kind of face image generation method based on GANs, comprises the following steps:

[0075] S1. Obtain a training set X, which consists of several face images.

[0076] In this embodiment, two methods of obtaining the training set X are given: the first is to cut the center of the CELEBA face data set into fixed-size face images such as: 64×64 96×64, etc.; the second One is to use crawler technology to crawl pictures of people on the public network, and then use face recognition technology to crop out face images, and finally scale the image size to a fixed size such as: 64×64 96×64, etc.

[0077] S2. Extract hidden features of all face images in the training set X to obtain a hidden feature set C of human face images.

[0078] S3, face image decoding training

[0079] S31. Sampling batchsize face images x sequentially without repeated sampling from the training ...

Embodiment 2

[0109] Please refer to image 3 , for step S2 in Embodiment 1, it specifically includes:

[0110] S21. Sampling is not repeated sequentially from the training set X (in the same epoch cycle, when the sequence of training set samples is determined, sequential sampling is non-repetitive sampling) batchsize face images x 1 ,x 2 ,...,x k (k=batchsize), and transform the pixel value scale to [-1,1] according to formula (5), the transformed image is still recorded as x 1 ,x 2 ,...,x k (k=batchsize).

[0111]

[0112] Among them, i is the label of an image in batchsize images, i∈[1, batchsize].

[0113] S22, using the batchsize face images after the pixel value scale transformation in step S21 to train the feature learning network, as follows:

[0114] Construct an initial feature learning network, and convert the batchsize pixel value scale-transformed image x in step S21 1 ,x 2 ,...,x k (k=batchsize) is fed into the feature learning network, and the mean square error l...

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Abstract

The invention discloses a method for generating face images based on GANs, which relates to the field of computer technology, wherein the face images generated by the generator can not only be associated with random vectors, but also be associated with feature vectors, indicating that the generated images have been directly trained The influence of image features increases interpretability; it can effectively avoid gradient disappearance, and it is possible to perform decoding training before binary confrontation training, which is beneficial to avoid gradient disappearance caused by optimizing JS divergence, thereby improving the quality of generated images; The decoder can learn good image structural features, so that the generator can learn better structural features, and then reduce the image of face distortion, and the clarity of the image can be learned more reasonably; due to the feature decoding constraint , which makes the gradient descent direction also subject to certain constraints when optimizing the objective function, enabling fewer epochs to be used in the training process.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method for generating face images based on GANs. Background technique [0002] The image generation method based on GANs is one of the hotspots in current artificial intelligence research. In theory, the GANs image generation method can effectively simulate many image contents, such as: human faces, buildings, indoor scenes, flowers, animal images, etc. The generation of these images also has practical significance. For example, the effective generation of real faces or cartoon faces can save the virtual generation of some general characters in film and television or animation works, thereby saving costs; the generation of indoor scenes can effectively protect certain The indoor background information that some photographers want to protect; the number of images of a certain category is small, and more images of this category can be obtained to achieve the purpose of ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T11/00G06K9/00G06K9/62G06N3/08G06N3/04
CPCG06T11/00G06N3/08G06V40/168G06N3/045G06F18/214
Inventor 和红杰陈泓佑陈帆
Owner SOUTHWEST JIAOTONG UNIV
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