Improvement and Application of Generative Adversarial Networks Algorithm Based on Transfer Learning

Abstract

Generative adversarial networks are currently used to solve various problems and are one of the most popular models. Generator and discriminator are characteristics of continuous game process in training. While improving the quality of generated pictures, it will also make it difficult for the loss function to be stable, and the training speed will be extremely slow compared with other methods. In addition, since the generative adversarial networks directly learns the data distribution of samples, the model will become uncontrollable and the freedom of the model will become too large when the original data distribution is constantly approximated. A new transfer learning training idea for the unsupervised generation model is proposed based on the generation network. The decoder of trained variational autoencoders is used as the network architecture and parameters to generative adversarial network generator. In addition, the standard normal distribution is obtained by sampling and then input into the model to control the degree of freedom of the model. Finally, we evaluated our method on using the MNIST, CIFAR10, and LSUN datasets. The experiment shows that our proposed method can make the loss function converge as quickly as possible and increase the model accuracy.

Generative Target Tracking Method with Improved Generative Adversarial Network

“…The second method, ROIAL, integrates GAN networks with MDResNet and MDNet, harnessing GAN-based learning to further enhance tracking performance. Both approaches aim to leverage ResNet's strengths and GAN-based learning to achieve superior results in the visual object tracking [16]. Zin [17] proposes a novel object tracking method that uses generative adversarial learning and incorporates distractors and a distractor generator into a Siamese network.…”