基于可解释性的知识提炼

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-10-30 DOI:10.1016/j.patcog.2024.111095
{"title":"基于可解释性的知识提炼","authors":"","doi":"10.1016/j.patcog.2024.111095","DOIUrl":null,"url":null,"abstract":"<div><div>Knowledge distillation (KD) is a popular approach for deep model acceleration. Based on the knowledge distilled, we categorize KD methods as label-related and structure-related. The former distills the very abstract (high-level) knowledge, e.g., logits; and the latter uses the spatial (low- or medium-level feature) knowledge. However, existing KD methods are usually not explainable, i.e., we do not know what knowledge is transferred during distillation. In this work, we propose a new KD method, Explainability-based Knowledge Distillation (Exp-KD). Specifically, we propose to use class activation map (CAM) as the explainable knowledge which can effectively capture both label- and structure-related information during the distillation. We conduct extensive experiments, including image classification tasks on CIFAR-10, CIFAR-100 and ImageNet datasets, and explainability tests on ImageNet and ImageNet-Segmentation. The results show the great effectiveness and explainability of Exp-KD compared with the state-of-the-art. Code is available at <span><span>https://github.com/Blenderama/Exp-KD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Explainability-based knowledge distillation\",\"authors\":\"\",\"doi\":\"10.1016/j.patcog.2024.111095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Knowledge distillation (KD) is a popular approach for deep model acceleration. Based on the knowledge distilled, we categorize KD methods as label-related and structure-related. The former distills the very abstract (high-level) knowledge, e.g., logits; and the latter uses the spatial (low- or medium-level feature) knowledge. However, existing KD methods are usually not explainable, i.e., we do not know what knowledge is transferred during distillation. In this work, we propose a new KD method, Explainability-based Knowledge Distillation (Exp-KD). Specifically, we propose to use class activation map (CAM) as the explainable knowledge which can effectively capture both label- and structure-related information during the distillation. We conduct extensive experiments, including image classification tasks on CIFAR-10, CIFAR-100 and ImageNet datasets, and explainability tests on ImageNet and ImageNet-Segmentation. The results show the great effectiveness and explainability of Exp-KD compared with the state-of-the-art. Code is available at <span><span>https://github.com/Blenderama/Exp-KD</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":49713,\"journal\":{\"name\":\"Pattern Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S003132032400846X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S003132032400846X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

知识提炼(KD)是深度模型加速的一种流行方法。根据所提炼的知识,我们将知识提炼方法分为标签相关和结构相关两类。前者提炼的是非常抽象(高层次)的知识,如对数;后者使用的是空间(低层或中层特征)知识。然而,现有的 KD 方法通常无法解释,也就是说,我们不知道在提炼过程中传输了哪些知识。在这项工作中,我们提出了一种新的 KD 方法,即基于可解释性的知识蒸馏(Exp-KD)。具体来说,我们建议使用类激活图(CAM)作为可解释知识,它能在蒸馏过程中有效捕捉标签和结构相关信息。我们进行了广泛的实验,包括 CIFAR-10、CIFAR-100 和 ImageNet 数据集上的图像分类任务,以及 ImageNet 和 ImageNet-Segmentation 上的可解释性测试。实验结果表明,与最先进的技术相比,Exp-KD 具有极高的有效性和可解释性。代码见 https://github.com/Blenderama/Exp-KD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Explainability-based knowledge distillation
Knowledge distillation (KD) is a popular approach for deep model acceleration. Based on the knowledge distilled, we categorize KD methods as label-related and structure-related. The former distills the very abstract (high-level) knowledge, e.g., logits; and the latter uses the spatial (low- or medium-level feature) knowledge. However, existing KD methods are usually not explainable, i.e., we do not know what knowledge is transferred during distillation. In this work, we propose a new KD method, Explainability-based Knowledge Distillation (Exp-KD). Specifically, we propose to use class activation map (CAM) as the explainable knowledge which can effectively capture both label- and structure-related information during the distillation. We conduct extensive experiments, including image classification tasks on CIFAR-10, CIFAR-100 and ImageNet datasets, and explainability tests on ImageNet and ImageNet-Segmentation. The results show the great effectiveness and explainability of Exp-KD compared with the state-of-the-art. Code is available at https://github.com/Blenderama/Exp-KD.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
期刊最新文献
Self-distillation with beta label smoothing-based cross-subject transfer learning for P300 classification L2T-DFM: Learning to Teach with Dynamic Fused Metric Image shadow removal via multi-scale deep Retinex decomposition ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels Consistency-driven feature scoring and regularization network for visible–infrared person re-identification
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1