Generative adversarial network based on Poincare distance similarity constraint: Focusing on overfitting problem caused by finite training data

Generative adversarial networks face harsh opposition between training data and model performance. In facing insufficient training data, the training process is extremely unstable, and the modules are difficult to converge, which increases the risk of model collapse, and finally manifests itself as poor image quality generated by the model. The essence of this problem is that a single discriminator is prone to overfitting. However, the traditional methods are to perform data augmentation, optimize the loss function, or improve the training strategy. In this paper, starting from the structural characteristics of the model itself, a dual-ways model with internal interactive learning and synchronous training is designed. The dual-ways discriminator directly maximizes the Poincare ' distance similarity, which is used to enhance the effectiveness of the error gradient and eliminate the risk of discriminator overfitting. Experimental results show that the dual-way model produces higher-quality images than the six well-known schemes in four benchmark datasets. The experimental results also show that thanks to the mechanism of internal interactive learning, the training process is more stable and the discriminator module converges in a timelier manner, which is the key to improving the performance of the model. Thus, we obtain a new stronger model that is different from the existing strategy.