降低模式崩与Monge-Kantorovich最佳运输生成对抗网络的降低模式崩
IEEE transactions on cybernetics
|August 3, 2023
概括
生成对抗性网络 (GAN) 的模式崩由Monge GAN解决. 这种新的方法将问题描述为Monge问题,有效地转换生成的数据分布以减少模式崩.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 模式崩是生成对抗网络 (GAN) 的一个重大挑战,限制了它们的实际应用.
- 现有的模式崩解决方案,如最佳运输 (OT) 策略和对发电机数或损失函数的修改,具有显著的局限性,包括梯度问题和模式冗余.
研究的目的:
- 提出一种新的方法,Monge GAN,以有效地减少生成对抗网络中的模式崩.
- 为了解决减轻模式崩现有方法的局限性.
主要方法:
- 在最佳运输 (OT) 中,制定模式崩作为Monge问题.
- 将Monge问题转换为适合GANs的分布转换问题.
- 使用修正的亲属神经网络作为分布转换的可测量函数.
- 使用康托罗维奇公式计算最佳运输 (OT) 成本作为分布之间的距离.
主要成果:
- 蒙格GAN成功地将生成的数据分布转换为原始数据分布.
- 对图像和数值数据集的广泛实验证明了Monge GAN在减少模式崩方面的有效性.
结论:
- 蒙格GAN为生成对抗网络中模式崩的持续挑战提供了一个有希望的解决方案.
- 提出的方法基于最佳运输理论,为改善GAN稳定性和性能提供了强大的框架.
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