自动编码生成对抗网络,以减少模式崩和增强特征表示
Yang Zou1, Yuxuan Wang1, Xiaoxiang Lu1
1Institute of Intelligence Science and Technology, School of Computer and Information, Hohai University, Nanjing 211100, China.
Entropy (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了一种自动编码的生成对抗网络 (GAN),以克服训练不稳定性和模式崩. 这种新的方法增强了特征表示,并确保了统一的数据分布,以提高生成模型性能.
科学领域:
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习 机器学习
背景情况:
- 生成对抗网络 (GAN) 是用于图像,视频,语音和文本处理的强大深度学习工具.
- 现有的GAN面临着模式崩和不稳定的训练等挑战,限制了它们的有效性.
- 解决这些局限性对于推进生成建模能力至关重要.
研究的目的:
- 提出一个新的自动编码生成对抗网络 (GAN) 架构.
- 为了增强特征表示和减轻GAN中的模式崩.
- 提高生成模型的稳定性和性能.
主要方法:
- 开发了一个自动编码GAN,包括生成器,区分器,编码器和解码器.
- 使用生成器用于多种模式的学习,以及用于样本区分的区分器.
- 使用编码器解码器将样本映射到嵌入空间并识别样本来源.
- 集成了一个集群算法和集群中心匹配以实现分布一致性.
主要成果:
- 拟议的自动编码GAN有效地减少了模式崩.
- 在模型中观察到增强的特征表示能力.
- 实验表明在保持数据分布的一致性方面表现优越.
- 视觉和定量结果都证实了模型的有效性.
结论:
- 自动编码GAN为GAN训练不稳定性和模式崩提供了强大的解决方案.
- 该模型显著改善了特征表示和分布一致性.
- 这一框架为各种生成性AI应用提供了有希望的进步.
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