在类内进行渐进式和适应式自蒸
Jianping Gou1, Jiaye Lin2, Lin Li2
1College of Computer and Information Science, College of Software, Southwest University, Chongqing, 400715, Chongqing, China.
概括
课内进步和自适应自蒸 (IPASD) 通过跨时代的知识转移来增强模型压缩. 这种新的自蒸方法提高了特征和逻辑知识,超过了现有的技术.
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:
- 深度神经网络面临越来越多参数的挑战,导致高计算负载和训练时间.
- 知识蒸 (KD) 是模型压缩的一个关键技术,培养高效的学生模型.
- 现有的自蒸方法 (离线KD,在线KD) 往往忽略了关键的特征和类别信息.
研究的目的:
- 引入一种新的自蒸方法,即类内渐进和自适应自蒸 (IPASD),以改进模型压缩.
- 通过整合特征级别和类别信息来解决现有的自蒸技术的局限性.
- 提高深度神经网络的效率和降低计算成本.
主要方法:
- IPASD在相邻的时代之间逐渐转移知识,重点是类内特征提取和紧性.
- 该方法整合了特征级和逻辑级的知识,利用了强大的教师知识.
- 通过使用基准真实标签作为监督信号来实现自适应优化.
主要成果:
- 与最先进的自蒸方法相比,IPASD显示出更高的性能.
- 该方法在知识传输和模型压缩方面显示出显著的改进.
- 对包括CIFAR-10,CIFAR-100,Tiny ImageNet,Plant Village和ImageNet在内的各种数据集进行了评估.
结论:
- IPASD提供了一种有效的自我蒸方法,增强模型压缩和知识传输.
- 该方法能够提取类型特征并促进类内紧性,这有助于其成功.
- 在高效的深度学习模型培训中,IPASD代表了显著的进步.
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