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 5 各模型在带钢表面缺陷数据集上的生成质量 |
Tab.5 Generated quality of each model on strip steel surface defect dataset |
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方法 | s | p1-NN | MMD | WD | DCGAN | 20 | 0.83 | 0.42 | 5.1 | DCGAN | 50 | 0.80 | 0.40 | 4.9 | DCGAN | 100 | 0.78 | 0.37 | 4.5 | DCGAN | 200 | 0.76 | 0.36 | 4.3 | WGAN-GP | 20 | 0.78 | 0.41 | 4.7 | WGAN-GP | 50 | 0.76 | 0.40 | 4.5 | WGAN-GP | 100 | 0.75 | 0.39 | 4.4 | WGAN-GP | 200 | 0.73 | 0.36 | 4.2 | FOGAN | 20 | 0.85 | 0.46 | 5.3 | FOGAN | 50 | 0.74 | 0.42 | 4.8 | FOGAN | 100 | 0.70 | 0.37 | 4.3 | FOGAN | 200 | 0.67 | 0.35 | 4.0 | NSGGAN | 20 | 0.59 | 0.28 | 3.6 | NSGGAN | 50 | 0.58 | 0.27 | 3.5 | NSGGAN | 100 | 0.58 | 0.24 | 3.2 | NSGGAN | 200 | 0.55 | 0.23 | 3.0 |
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