一个双分支模式,具有跨行业和行业内部的对比损失,用于长尾识别
Qiong Chen1, Tianlin Huang2, Geren Zhu1
1School of Computer Science and Engineering, South China University of Technology, China.
概括
本研究介绍了双分支长尾识别 (DB-LTR),这是一个不平衡数据集的新型模型. 在长尾分布中,DB-LTR有效地提高了模型适应性.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 现实世界数据集经常表现出长尾分布,其中头类主导数据,而尾类代表很少.
- 在这种不平衡的数据上训练的模型与尾部类识别斗争,导致模两可的决策边界.
研究的目的:
- 提出一种新且有效的模型,双分支长尾识别 (DB-LTR),以应对长尾数据分布的挑战.
- 提高模型适应性和表现在代表不充分的尾部类.
主要方法:
- 引入了DB-LTR,这是一个双分支架构,包括一个不平衡的学习分支和一个对比的学习分支 (CoLB).
- 不平衡学习分支使用共享的骨干和线性分类器,并使用标准的不平衡学习技术.
- CoLB专注于学习尾巴类原型,并使用跨行业对比损失,行业内部对比损失和度量损失.
主要成果:
- DB-LTR在适应尾部级别方面表现出更好的能力.
- 该模型有助于学习具有良好代表性的特征空间和歧视性决策边界.
- 在CIFAR100-LT,ImageNet-LT和Places-LT数据集上的实验表明DB-LTR的表现优于比较方法.
结论:
- 对于长尾识别问题,DB-LTR提供了一个简单而有效的解决方案.
- 拟议的对比学习分支显著提高了尾部类识别性能.
- DB-LTR在基准长尾数据集上取得了竞争力和优异的结果.
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