重新权衡平衡表示学习,用于多个领域的长尾图像识别
Panpan Fu1,2, Nur Intan Raihana Ruhaiyem3, Jiangtao Wang2
1School of Informatics and Engineering, Suzhou University, Suzhou, 234000, China.
Scientific reports
|July 4, 2025
概括
本研究介绍了一个平衡表示学习 (BRL) 算法,以解决图像识别多域学习中的数据不平衡问题. BRL有效地减少了偏差,改善了分类器的性能,特别是在具有挑战性的类中.
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:
- 多域长尾学习存在挑战,原因是域内类不平衡和跨域样本比例变化.
- 这些不平衡在共变量和表示学习中引入了显著的偏差,阻碍了域不变特征的提取.
研究的目的:
- 应用和评估用于多域长尾图像识别的先进重量调整平衡表示学习 (BRL) 算法.
- 为了解决因数据不平衡而导致的输入和潜在空间中的偏差.
主要方法:
- 该研究将共变量和表示平衡技术集成到基于重权的类平衡框架中.
- 平衡表示学习 (BRL) 算法应用于多域长尾图像识别任务.
主要成果:
- 对六个基准数据集的广泛评估表明了BRL的有效性.
- 该算法成功地提取了域和类无偏见的特征表示.
- 观察到分类器性能显著改善,特别是在最不平衡的类中.
结论:
- 均衡表示学习 (BRL) 算法为多域长尾图像识别提供了强大的解决方案.
- 这种方法对环境监测和医疗成像等领域的应用具有前景,解决了关键数据不平衡问题.
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