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In recent years, multi-scale generative adversarial networks (GANs) have beenproposed to build generalized image processing models based on single sample.Constraining on the sample size, multi-scale GANs have much difficultyconverging to the global optimum, which ultimately leads to limitations intheir capabilities. In this paper, we pioneered the introduction of PAC-Bayesgeneralized bound theory into the training analysis of specific models underdifferent adversarial training methods, which can obtain a non-vacuous upperbound on the generalization error for the specified multi-scale GAN structure.Based on the drastic changes we found of the generalization error bound underdifferent adversarial attacks and different training states, we proposed anadaptive training method which can greatly improve the image manipulationability of multi-scale GANs. The final experimental results show that ouradaptive training method in this paper has greatly contributed to theimprovement of the quality of the images generated by multi-scale GANs onseveral image manipulation tasks. In particular, for the image super-resolutionrestoration task, the multi-scale GAN model trained by the proposed methodachieves a 100% reduction in natural image quality evaluator (NIQE) and a 60%reduction in root mean squared error (RMSE), which is better than many modelstrained on large-scale datasets.

Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory