通过在线意见分歧最小化进行自适应域名通用化
概括
域泛化 (DG) 模型在分配转移方面遇到了困难. 我们的AdaODM框架在测试时自适应性地修改模型,通过最小化分类器分歧来提高未见域的性能.
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:
- 由于训练和部署之间的分布转移,深度神经网络的性能下降.
- 现有的域泛化 (DG) 方法往往无法超过简单的经验风险最小化 (ERM).
研究的目的:
- 提出一个新的框架,AdaODM,在测试时适应性修改源模型,以改善对未见域的概括.
- 通过提供一致的性能改进来增强现有的GD算法.
主要方法:
- AdaODM采用一个共享的域名通用特征提取器和多个域名特定的分类器进行对抗训练.
- 特征提取器将样本映射到域不变空间,而分类器捕获域特定的决策边界.
- 在测试时,AdaODM微调源模型以最大限度地减少分类器之间的预测分歧,对准目标特征.
主要成果:
- 通过AdaODM,总局不断提高ERM和CORAL等方法的通用化能力.
- 该框架在四个总局的基准指标中取得了最先进的表现:VLCS,PACS,OfficeHome和TerraIncognita.
- 测试时的自适应修改有效地解决了目标和源域之间的分布差异.
结论:
- AdaODM提供了一种通用和直角的方法来增强现有的GD算法.
- 适应性测试时间修改策略对于稳定地将模型转移到未见的领域至关重要.
- 这一框架代表了在分配轮班下实现可靠绩效的重大进步.
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