相关实验视频
通过文本噪音融合和动态对抗温度的数据自由知识蒸
Deheng Zeng1, Zhengyang Wu2, Yunwen Chen3
1School of Computer Science, South China Normal University, Guangzhou, 510631, Guangdong, China; School of Artificial Intelligence, South China Normal University, Foshan, 528225, Guangdong, China.
概括
本研究引入了一种新的无数据知识蒸方法,使用文本嵌入和动态温度调整. 它产生更高质量的,多样化的样本,以便在没有原始数据的情况下有效地转移知识.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 无数据知识蒸 (DFKD) 允许在没有原始数据的情况下进行知识传输.
- 现有的DFKD方法由于缺乏语义信息的噪音而与低质量的样本作斗争.
- 在对抗性训练中的固定温度限制了合成数据的多样性.
研究的目的:
- 提出一种新的DFKD方法,即文本-噪声融合和动态对抗温度 (TNFDAT).
- 提高DFKD的样本质量和多样性.
- 提高学生网络的普遍性和稳定性.
主要方法:
- 将随机噪声与类特定文本嵌入 (CSTE) 结合起来,用于样本生成.
- 采用对发电机对抗训练温度的动态调整.
- 介绍了一个基于信息的适应性样本权重策略.
主要成果:
- 通过利用CSTE,TNFDAT产生了更高质量的培训样本.
- 动态温度调整显著提高了合成样本的多样性.
- 适应性样本权重提高了知识提炼的有效性和学生网络的稳定性.
结论:
- TNFDAT的性能优于最先进的DFKD方法.
- 提出的方法有效地解决了样品质量和样品多样性的局限性.
- 在不需要原始数据的情况下,TNFDAT为知识蒸提供了一个强大的框架.
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