While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs.

研究评估了生成对抗网络引起的网络对网络暴力对人类的财政、社会、精神、文化、语法和皮肤影响，提出了一种更和平的方法——生成非对抗网络（GUNs），使用博弈论为理论和电子模型可获得公正分数的情况下，同时训练生成器和激励模型，实现和谐共处和梦想的实现，并将更多的GUNs推广到网络上来解决这一问题。

制止 GAN 暴力: 生成非对抗网络