通过联合瓦瑟斯坦距离最小化来学习域不变表示
Léo Andéol1, Yusei Kawakami2, Yuichiro Wada3
1Machine Learning group, Technische Universität Berlin, 10587 Berlin, Germany; Berlin Institute for the Foundations of Learning and Data - BIFOLD, 10587 Berlin, Germany.
概括
本研究引入了机器学习 (ML) 模型的新理论基础,使其在不同数据域中保持一致的性能. 将ML损失与GAN类别的区分器相结合,可以提高域不变性和预测稳定性.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
- 统计 统计 统计 统计
背景情况:
- 训练数据的领域转移在现实世界的机器学习应用中很普遍.
- 标准的机器学习损失无法保证在不同数据领域的一致性能.
- 确保域不变表示对于强大的模型概括至关重要.
研究的目的:
- 建立新的理论基础,以应对机器学习领域的转变.
- 以数学方式将经典的监督机器学习损失与瓦瑟斯坦距离联系起来.
- 开发方法来改善域不变性和预测稳定性.
主要方法:
- 在联合空间中开发了监督机器学习损失和瓦瑟斯坦距离之间的理论关系.
- 集成了一个生成对抗网络 (GAN) 类型的歧视器来执行域不变性.
- 使用分类和回归损失与区分器结合使用.
主要成果:
- 证明了组合损失和GAN类型的区分器提供了域间真正的瓦瑟斯坦距离的上限.
- 实现了更多的不变表示和跨域稳定的预测性能.
- 在多个图像数据集上经验验证的理论结果.
结论:
- 拟议的方法增强了域不变性,从而导致更稳定的预测性能.
- 该方法在各个领域系统地产生了更高的最低分类准确性.
- 这项工作为构建更强大的机器学习模型以应对领域转变提供了理论和经验基础.
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