基于条件梯度Wasserstein生成对抗网络的图像识别

生成式对抗网络GAN功能强大，但是具有收敛速度慢、训练不稳定、生成样本多样性不足等缺点。该文结合条件深度卷积对抗网络CDCGAN和带有梯度惩罚的Wasserstein生成对抗网络WGAN-GP的优点，提出了一个混合模型-条件梯度Wasserstein生成对抗网络CDCWGAN-GP，用带有梯度惩罚的Wasserstein距离训练对抗网络保证了训练稳定性且收敛速度更快，同时加入条件c来指导数据生成。另外为了增强判别器提取特征的能力，该文设计了全局判别器和局部判别器一起打分，最后提取判别器进行图像识别。实验结果证明，该方法有效的提高了图像识别的准确率。

Image Recognition With Conditional Wasserstein Generative Adversarial Networks with gradient penalty*

Generated adversarial net GAN is powerful,but it has some disadvantages such as slow convergence, unstable training, and insufficient sample diversity.This paper presents a conditional gradient Wasserstein generation confrontation network model CDCWGAN-GP by Combining the advantage of conditional deep convolution adversarial net CDCGAN and Wasserstein generated adversarial net with gradient penalty WGAN-GP. Using the Wasserstein distance training against the network with gradient penalty guarantees training stability and faster convergence, while adding condition c to guide data generation. In addition, in order to enhance the ability of the discriminator to extract features, the paper designs a global discriminator and a local discriminator to score together, and finally extracts the discriminator for image recognition. The result of simulation experiments show that this method effectively improves the accuracy of image recognition.