基于随机决策规则的生成对抗网络的新范式
Sehwan Kim1, Qifan Song1, Faming Liang1
1Department of Statistics, Purdue University, West Lafayette, IN 47907.
概括
本研究引入了一种新的生成对抗网络 (GAN) 公式,以解决模式崩,增强数据多样性. 拟议的方法使用随机决策规则和经验贝叶斯方法来实现稳定的训练和接近纳什平衡.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 统计 统计 统计 统计
背景情况:
- 生成对抗网络 (GAN) 是强大的培训生成模型,但遭受模式崩,限制生成的数据多样性.
- 在GAN中,模式崩导致生成样本缺乏多样性,阻碍了它们的应用.
- 现有的GAN培训方法在实现稳定的融合和多样化的输出方面面临挑战.
研究的目的:
- 确定GAN中模式崩的根本原因.
- 通过使用随机决策规则,提出一种新的GAN配方来解决模式崩.
- 开发一种基于实证贝叶斯原理的培训方法,以提高GAN性能.
主要方法:
- 引入了一种新的GAN配方与随机决策规则,导致分辨器收和生成器收到纳什平衡分布.
- 提出了一种经验性的贝叶斯式训练方法,将区分器视为超参数.
- 利用一个随机梯度马尔科夫链蒙特卡洛 (MCMC) 算法来模拟生成器和随机梯度下降来进行区分器更新.
主要成果:
- 建立了拟议方法与纳什平衡的理论趋同.
- 证明了该方法在解决模式崩和改善生成数据多样性的有效性.
- 成功地将该方法应用于图像生成,非参数聚类和非参数条件独立性测试.
结论:
- 拟议的GAN配方和培训方法有效地克服了模式崩.
- 经验贝叶斯方法与MCMC和SGD提供了一个稳定和融合的培训策略.
- 该方法显示出超出图像生成范围的广泛应用,包括统计任务.
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