通过明智地利用开放式数据集进行强大的半监督学习
概括
智能开放式半监督学习 (WiseOpen) 选择性地使用未标记的数据来提高模型性能. 这种方法可以过出有问题的分布外数据,在现实的场景中提高分类准确性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 开放式半监督学习 (OSSL) 面临着与分布外 (OOD) 数据的挑战,这可能会降低标准模型中的性能.
- 现有的OSSL方法经常使用所有开放式数据,包括潜在的有害样本,影响模型的稳定性.
- 需要制定有效处理在OSSL设置中的OD数据的策略.
研究的目的:
- 为OSSL开发一个强大的数据选择策略,以改善分布式 (ID) 分类.
- 提出一个通用的OSSL框架,WiseOpen,可以选择性地利用开放数据集.
- 在现实的场景中提高OSSL模型的性能和稳定性.
主要方法:
- 建议Wise开放式半监督学习 (WiseOpen),这是一个利用渐变变差选择机制的框架.
- 选择性地训练模型,使用一个精选的开放数据集子集,不包括不友好的样本.
- 引入两个实用变体:低频更新和基于损失的选择,以提高计算效率.
主要成果:
- 与最先进的OSSL方法相比,WiseOpen表现出优越的性能.
- 基于梯度变异的选择有效地识别和利用有益的开放集数据.
- 实验结果验证了WiseOpen及其变体在增强ID分类方面的有效性.
结论:
- 在OSSL中选择性数据利用对于减轻OOD数据的负面影响至关重要.
- 智能开放提供了一个理论上有基础的,实际上有效的方法来强大的OSSL.
- 拟议的方法提高模型性能,并通过其变体提供计算优势.
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