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

CAO Zhiyi; NIU Shaozhang; ZHANG Jiwei

doi:10.11999/JEIT170357

Volume 40 Issue 2

Feb. 2018

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CAO Zhiyi, NIU Shaozhang, ZHANG Jiwei. Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning[J]. Journal of Electronics & Information Technology, 2018, 40(2): 323-330. doi: 10.11999/JEIT170357

Citation:

CAO Zhiyi, NIU Shaozhang, ZHANG Jiwei. Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning[J]. Journal of Electronics & Information Technology, 2018, 40(2): 323-330. doi: 10.11999/JEIT170357

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

doi: 10.11999/JEIT170357 cstr: 32379.14.JEIT170357

Funds:

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

Received Date: 2017-04-20
Rev Recd Date: 2017-10-17
Publish Date: 2018-02-19

Abstract

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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References(21)

References

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