一个统一的深域适应框架:推进特征分离性和局部对齐性
Pranav Kumar1, Jimson Mathew1, Rakesh Kumar Sanodiya2
1Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar 801106, India.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
转移学习的领域转移由DDASLA解决,这是一个改进特征提取和对齐的新型框架. 实验表明DDASLA增强了跨领域的模型概括性和稳定性.
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:
- 域转移是域适应的一个关键挑战,它来自源域和目标域之间的显著数据分布差异.
- 现有的域调整方法有可能改变内在数据属性.
- 有效的域调整对于改善未见数据分布上的模型性能至关重要.
研究的目的:
- 引入一个新的统一深域适应框架 (DDASLA),以应对域转移.
- 在深度学习模型中增强特征提取和对齐能力,以进行域调整.
- 改进跨不同数据域的模型概括性和稳定性.
主要方法:
- 在ResNet18架构中整合注意力机制,特别是自我注意力,以改进特征提取.
- 使用一个组合损失函数,包括特征歧视的角损失,用于局部分布对齐的局部最大平均差异 (LMMD),以及用于决策边界精细化的最小化.
- 开发一个统一的深域适应框架 (DDASLA).
主要成果:
- 在办公室和遥感数据集上,DDASLA与几种最先进的方法相比表现出了更好的表现.
- 拟议的方法有效地改善了特征分离性和域间的局部对齐.
- 实验结果验证了注意力增强的ResNet18模型的增强特征提取能力.
结论:
- DDASLA提供了一种有效的解决方案,可以缓解转移学习中的领域转移.
- 该框架显著提高了不同领域的模型通用性和稳定性.
- 这些发现为未来深度域适应研究提供了基础.
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