
A novel first-order method based on the Gauss-Newton approach is proposed to efficiently solve the min-max optimization problem in training generative adversarial networks (GANs). The method uses a fixed-point iteration with a Gauss-Newton preconditioner and achieves state-of-the-art performance on image generation tasks while maintaining computational efficiency.


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efficient-gauss-newton-approach-for-training-generative-adversarial-networks


Efficient Gauss-Newton Approach for Training Generative Adversarial Networks
