MAGAN: Margin Adaptation for Generative Adversarial Networks
This addresses stability issues in GAN training for image generation, but appears incremental as it builds on existing hinge loss methods with adaptive margins.
The paper tackled the problem of improving stability and performance in Generative Adversarial Networks (GANs) by proposing the MAGAN algorithm, which uses an adaptive hinge loss function and achieved qualitative and quantitative improvements in unsupervised image generation compared to state-of-the-art methods.
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive principled criteria for when to update the margin. We prove that our method converges to its global optimum under certain assumptions. Evaluated on the task of unsupervised image generation, the proposed training procedure is simple yet robust on a diverse set of data, and achieves qualitative and quantitative improvements compared to the state-of-the-art.