对长尾动物视觉识别进行概率学对比学习
IEEE transactions on pattern analysis and machine intelligence
|February 23, 2024
概括
本研究引入了概率对比 (ProCo) 学习,以解决机器学习中的数据不平衡问题. 通过估计特征分布,ProCo有效地处理长尾分布,在视觉识别任务中表现优于现有的方法.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 现实世界的数据通常表现出长尾分布,许多少数阶级的样本很少.
- 这种不平衡显著降低了标准的监督学习算法.
- 监督对比学习显示出不平衡的希望,但需要大批量,这在不平衡的数据中很难.
研究的目的:
- 提出一种新的概率对比 (ProCo) 学习算法,以克服对不平衡数据集的监督对比学习的局限性.
- 通过估计类特征分布,通过较小批量实现有效的对比学习.
- 提高受数据不平衡影响的任务的性能.
主要方法:
- 提出了一个概率对比 (ProCo) 学习算法.
- 假设的正常化特征遵循von Mises-Fisher (vMF) 分布的混合.
- 使用在线计算的第一个样本时刻估计的分布参数.
- 从估计的分布中采样了对比对,并从优化中获得了预期的对比损失.
- 通过生成伪标签,将ProCo扩展到半监督学习.
主要成果:
- ProCo成功地解决了在不平衡的数据集中构建对比对的挑战.
- 使用vMF分布可以有效估计和采样对比对.
- 在监督和半监督的视觉识别和物体检测任务中,ProCo 始终表现出与现有方法相比的优势.
- 进行了对ProCo错误界限的理论分析.
结论:
- 概率对比 (ProCo) 学习为机器学习中的数据不平衡提供了一个强大的解决方案.
- 拟议的方法有效地利用特征分布估计,以改善对比学习.
- 在各种计算机视觉任务中,ProCo显示了广泛的适用性和卓越的性能.
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