基于生成对抗网络的太阳能电池缺陷增强方法
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刘坤,文熙,黄闽茗,杨欣欣,毛经坤
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Solar cell defect enhancement method based on generative adversarial network
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Kun LIU,Xi WEN,Min-ming HUANG,Xin-xin YANG,Jing-kun MAO
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表 6 各模型在DAGM2007数据集上的生成质量 |
Tab.6 Generated quality of each model on DAGM2007 dataset |
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方法 | s | p1-NN | MMD | WD | DCGAN | 20 | 0.88 | 0.53 | 5.4 | DCGAN | 50 | 0.87 | 0.51 | 5.2 | DCGAN | 100 | 0.85 | 0.50 | 5.5 | DCGAN | 200 | 0.83 | 0.47 | 4.8 | WGAN-GP | 20 | 0.85 | 0.51 | 4.9 | WGAN-GP | 50 | 0.83 | 0.47 | 4.6 | WGAN-GP | 100 | 0.82 | 0.45 | 4.5 | WGAN-GP | 200 | 0.80 | 0.44 | 4.3 | FOGAN | 20 | 0.90 | 0.56 | 5.8 | FOGAN | 50 | 0.86 | 0.50 | 5.5 | FOGAN | 100 | 0.83 | 0.46 | 5.0 | FOGAN | 200 | 0.78 | 0.43 | 4.5 | NSGGAN | 20 | 0.75 | 0.46 | 4.4 | NSGGAN | 50 | 0.73 | 0.43 | 4.2 | NSGGAN | 100 | 0.70 | 0.42 | 4.0 | NSGGAN | 200 | 0.68 | 0.40 | 3.9 |
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