Abstract
Mode collapse has always been a fundamental problem in generative adversarial networks. The recently proposed Zero Gradient Penalty (0GP) regularization can alleviate the mode collapse, but it will exacerbate a discriminator’s misjudgment problem, that is the discriminator judges that some generated samples are more real than real samples. In actual training, the discriminator will direct the generated samples to point to samples with higher discriminator outputs. The serious misjudgment problem of the discriminator will cause the generator to generate unnatural images and reduce the quality of the generation. This paper proposes Real Sample Consistency (RSC) regularization. In the training process, we randomly divided the samples into two parts and minimized the loss of the discriminator’s outputs corresponding to these two parts, forcing the discriminator to output the same value for all real samples. We analyzed the effectiveness of our method. The experimental results showed that our method can alleviate the discriminator’s misjudgment and perform better with a more stable training process than 0GP regularization. Our real sample consistency regularization improved the FID score for the conditional generation of Fake-As-Real GAN (FARGAN) from 14.28 to 9.8 on CIFAR-10. Our RSC regularization improved the FID score from 23.42 to 17.14 on CIFAR-100 and from 53.79 to 46.92 on ImageNet2012. Our RSC regularization improved the average distance between the generated and real samples from 0.028 to 0.025 on synthetic data. The loss of the generator and discriminator in standard GAN with our regularization was close to the theoretical loss and kept stable during the training process.
Highlights
Since the generative adversarial network proposed by Goodfellow [1] in 2014, it has achieved great development [2,3] and has been applied in many ways [4,5,6,7,8,9], such as image inpainting, super-resolution reconstruction, style transfer, and image editing
Due to the 0GP regularization, there will be more cases where the discriminator’s gradient at the real samples is less than the discriminator’s gradient at the generated samples during the training process; We propose Real Sample Consistency (RSC) regularization, forcing the discriminator to output the same value for all real samples
To verify the effectiveness of our proposed real sample consistency regularization, we experimented on synthetic data, CIFAR-10, CIFAR-100, and ImageNet2012
Summary
Since the generative adversarial network proposed by Goodfellow [1] in 2014, it has achieved great development [2,3] and has been applied in many ways [4,5,6,7,8,9], such as image inpainting, super-resolution reconstruction, style transfer, and image editing. Thanh-Tung [13] argued that the generated samples and the real samples in the later training stage are very similar, but the discriminator can distinguish between the real samples and the generated samples, resulting in a gradient explosion. In this case, the generator’s gradient in the minibatch points to samples where the gradient explodes and mode collapse occurs. Due to the 0GP regularization, there will be more cases where the discriminator’s gradient at the real samples is less than the discriminator’s gradient at the generated samples during the training process; We propose Real Sample Consistency (RSC) regularization, forcing the discriminator to output the same value for all real samples. Experiments on synthetic and real-world datasets verified that our method achieves better performance than 0GP regularization
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