基于半監(jiān)督學習生成對抗網(wǎng)絡的人臉還原算法研究
doi: 10.11999/JEIT170357 cstr: 32379.14.JEIT170357
基金項目:
國家自然科學基金(61370195, U1536121)
Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning
Funds:
The National Natural Science Foundation of China (61370195, U1536121)
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摘要: 基于大量訓練樣本生成高置信度圖像的生成對抗網(wǎng)絡研究已經(jīng)取得一些成果，但是現(xiàn)有的研究只針對已知訓練樣本進行圖像生成，而未將訓練的參數(shù)用于訓練樣本之外的圖像生成。該文設計了一種改進的生成對抗網(wǎng)絡模型，在已有網(wǎng)絡的基礎上增加一個還原層，使得測試圖像可以通過改進的對抗網(wǎng)絡生成對應的高置信度圖像。實驗結果表明，改進的生成對抗網(wǎng)絡參數(shù)可以應用到訓練集之外的普通樣本。同時本文改進了生成模型的損失算法，極大地縮短了網(wǎng)絡的收斂時間。
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關鍵詞:
- 生成對抗網(wǎng)絡 /
- 半監(jiān)督學習 /
- 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.-
Key words:
- Generative adversarial nets /
- Semisupervised learning /
- Generative model /
- Loss function
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