ChatDiff:一个基于ChatGPT的扩散模型,用于长尾分类
Chenxun Deng1, Dafang Li1, Lin Ji1
1School of Technology, Beijing Forestry University, Beijing, 100083, PR China; Research Center for Biodiversity Intelligent Monitoring, Beijing Forestry University, Beijing, 100083, PR China; State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, 100083, PR China.
概括
通过使用ChatGPT和扩散模型生成多样化的数据样本,ChatDiff增强了不平衡数据集的深度学习. 这种方法有效地解决了代表性不足的阶级的数据稀缺问题,同时删除了有害的负样本.
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:
- 长尾数据分布对深度学习应用提出了重大挑战.
- 现有的数据增强方法在样本多样性和负样本干扰方面扎.
研究的目的:
- 介绍ChatDiff,一种新的信息增强方法,用于改善对不平衡数据集的深度学习.
- 为代表性不足的阶级生成多样化的积极样本,并消除有害的负面样本.
主要方法:
- 使用提示模板从ChatGPT-3.5提取文本知识,以丰富功能空间.
- 采用条件扩散模型,为尾部类生成语义丰富的图像样本.
- 实施基于CLIP的区分器来过和删除生成的负样本.
主要成果:
- 聊天Diff成功地为代表性不足的阶级生成多样化和语义丰富的样本.
- 通过CLIP区分器去除负样本,可以防止学习错误的特征.
- 在多个基准指标的长尾分类表现中表现出显著的改善.
结论:
- 在深度学习中,ChatDiff为长尾数据问题提供了有效的解决方案.
- 大型语言模型和扩散模型与歧视性过的集成增强了数据增强.
- 在CIFAR10-LT,CIFAR100-LT,ImageNet-LT和iNaturalist 2018数据集上验证了有效性.
相关概念视频
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