• Corpus ID: 243938343

Training Generative Adversarial Networks with Adaptive Composite Gradient

@article{Qi2021TrainingGA,
  title={Training Generative Adversarial Networks with Adaptive Composite Gradient},
  author={Huiqing Qi and Fang Li and Shengli Tan and Xiangyun Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.05508}
}
The wide applications of Generative adversarial networks benefit from the successful training methods, guaranteeing that an object function converges to the local minima. Nevertheless, designing an efficient and competitive training method is still a challenging task due to the cyclic behaviors of some gradient-based ways and the expensive computational cost of these methods based on the Hessian matrix. This paper proposed the adaptive Composite Gradients (ACG) method, linearly convergent in… 
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