SCIENTIA SINICA Informationis, Volume 51 , Issue 4 : 602(2021) https://doi.org/10.1360/SSI-2019-0205

## Instability analysis for generative adversarial networks and its solving techniques

• AcceptedMar 1, 2020
• PublishedNov 20, 2020
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### References

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• Figure 1

(Color online) Different $L$ value and its corresponding loss function variation when $\lambda=10$ (CIFAR-10).protectłinebreak (a) Discriminator loss $D$_loss; (b) generator loss $G$_loss

• Figure 2

(Color online) Comparison of the loss function variation and the general variation trend of gradient between two penalty techniques. (a) The variation of loss function; (b) the general variation trend of gradient

• Figure 3

(Color online) The variation of loss function in three algorithms. (a) The variation of loss function in discriminator (critic function); (b) the variation of loss function in generator

• Figure 4

(Color online) The general variation trend of gradient in three algorithms. (a) The general variation trend of gradient in discriminator (critic function); (b) the general variation trend of gradient in generator

• Table 1   Different $\lambda$ and its corresponding $\rm~FID$ value when $L=0$ (CIFAR-10)
 $\lambda$ 5 10 20 50 100 $\rm~FID$ 15.41 12.91 16.28 34.61 32.82
• Table 2   Different $L$ and its corresponding $\rm~FID$ value when $\lambda=10$ (CIFAR-10)
 $L$ 10 5 1 0.5 0 $\rm~FID$ 18.59 18.56 16.47 15.4 12.91
• Table 3   Algorithms performance comparison (The smaller the FID value, the better)
 CIFAR-10 SVHN STL-10 CelebA LSUN (bedroom) LSGAN (2017) 22.20 3.84 20.17 5.10 5.23 SNGAN (2018) 20.70 4.53 18.11 5.56 12.05 WGAN-GP (2017) 21.89 4.09 18.19 5.01 14.61 WGAN-LP (2018) 21.01 3.62 17.40 5.12 15.21 GAN-0GP (2019) 18.91 6.10 14.49 4.53 7.14 Ours 12.91 2.78 12.85 3.91 6.85 Real datasets 0.46 0.24 0.84 0.34 0.55

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