通过潜结构感知序列自动编码器演变域泛化
IEEE transactions on pattern analysis and machine intelligence
|September 29, 2023
概括
本研究介绍了对非静止环境的演变域泛化,并建议MMD-LSAE处理连续域漂移以提高机器学习模型性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 域泛化 (DG) 传统上针对静止环境中的分布外 (OOD) 数据.
- 像自动驾驶这样的现实应用面临着不断演变的领域漂移,这对现有的 DG 方法构成挑战.
研究的目的:
- 解决目前在非静止环境中的总局方法的局限性.
- 引入和验证一个新的框架,用于不断发展的域泛化.
主要方法:
- 提出MMD-LSAE,这是一个旨在捕捉非静止领域不断变化的模式的框架.
- 描述OOD数据转移到共变量和概念转移,使用深度自动编码器推断它们的动态.
- 通过最大平均差异 (MMD) 优化潜伏空间分布,以对齐前后.
主要成果:
- MMD-LSAE有效地捕捉了不断变化的域模式,以提高概括性.
- 该框架通过隐式地促进相互信息最大化来证明优越的代表性学习.
- 在合成和现实数据上的实验结果证实了该方法的良好表现.
结论:
- MMD-LSAE提供了一个强大的解决方案,用于在非静止设置中演变域泛化.
- 该方法提高了模型适应持续域漂移的适应性.
- 这项工作推进了动态现实世界的应用领域的域概括领域.
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