通过风格偏差的因果推理 解混为一谈域名通用化
IEEE transactions on pattern analysis and machine intelligence
|January 12, 2026
概括
本研究引入了风格解混因果学习 (SDCL),以提高深度神经网络的可靠性,使用分布外数据. SDCL有效地减少了风格偏差,增强了视觉应用程序的域泛化.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 面临着分布外数据的挑战,限制了现实世界的视觉应用程序可靠性.
- 现有的域泛化方法往往忽视了风格频率的影响,导致虚假的相关性和推断可靠性降低.
研究的目的:
- 引入一种基于因果推断的新型框架,即风格解惑因果学习 (SDCL),以增强图像模式中的域概括.
- 解决风格作为一种混因素,改善因果表示的学习,而不是虚假的相关性.
主要方法:
- 构建一个结构因果模型 (SCM) 用于域概括,并应用后门调整以影响风格.
- 设计一种以风格为指导的专家模块 (SGEM),用于风格分布的自适应集群.
- 在特征提取过程中实施后门因果学习模块 (BDCL) 进行因果干预.
主要成果:
- 通过确保混风格的公平整合,SDCL框架有效地减少了风格偏见.
- 实验表明,在多域和单域泛化场景中,性能优越.
- 该方法在各种自然和医学图像识别任务中显示出有效性.
结论:
- 通过明确处理风格混因素,SDCL提供了一种多功能且有效的解决方案,用于增强DNN中的域名泛化.
- 基于因果推理的方法在面对领域转移时提高了模型的稳定性和可靠性.
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