基于半監(jiān)督學(xué)習(xí)生成對(duì)抗網(wǎng)絡(luò)的人臉還原算法研究

曹志義; 牛少彰; 張繼威

doi:10.11999/JEIT170357

基于半監(jiān)督學(xué)習(xí)生成對(duì)抗網(wǎng)絡(luò)的人臉還原算法研究

doi: 10.11999/JEIT170357

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61370195, U1536121)

計(jì)量
- 文章訪問數(shù): 1551
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-04-20
- 修回日期: 2017-10-17
- 刊出日期: 2018-02-19

Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning

Funds:

The National Natural Science Foundation of China (61370195, U1536121)

摘要

摘要: 基于大量訓(xùn)練樣本生成高置信度圖像的生成對(duì)抗網(wǎng)絡(luò)研究已經(jīng)取得一些成果，但是現(xiàn)有的研究只針對(duì)已知訓(xùn)練樣本進(jìn)行圖像生成，而未將訓(xùn)練的參數(shù)用于訓(xùn)練樣本之外的圖像生成。該文設(shè)計(jì)了一種改進(jìn)的生成對(duì)抗網(wǎng)絡(luò)模型，在已有網(wǎng)絡(luò)的基礎(chǔ)上增加一個(gè)還原層，使得測(cè)試圖像可以通過(guò)改進(jìn)的對(duì)抗網(wǎng)絡(luò)生成對(duì)應(yīng)的高置信度圖像。實(shí)驗(yàn)結(jié)果表明，改進(jìn)的生成對(duì)抗網(wǎng)絡(luò)參數(shù)可以應(yīng)用到訓(xùn)練集之外的普通樣本。同時(shí)本文改進(jìn)了生成模型的損失算法，極大地縮短了網(wǎng)絡(luò)的收斂時(shí)間。
- 生成對(duì)抗網(wǎng)絡(luò) /
- 半監(jiān)督學(xué)習(xí) /
- 生成模型 /
- 損失函數(shù)
Abstract: Based on a large number of training samples to generate high confidence images, generative adversarial nets achieve good results, but the existing network of image generation in the training sample basis, the training parameters can not be used to generate images outside of training samples. In this paper, an improved generative adversarial nets model is proposed, and a reduction layer is added on the basis of the existing network, so that the test image can generate the corresponding high confidence image through the improved generative adversarial nets. The experimental results show that the improved generative adversarial nets parameters can be applied to the common samples outside the training set. At the same time, this paper improves the loss algorithm of the generated model, which greatly shortens the convergence time of the network.
- Generative adversarial nets /
- Semisupervised learning /
- Generative model /
- Loss function

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參考文獻(xiàn)(21)

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