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Towards Prior Gap and Representation Gap for Long-tailed Recognition, Pattern Recognition | |
Zhang Ming-Liang1,2![]() ![]() ![]() | |
发表期刊 | Pattern Recognition
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ISSN | 0031-3203 |
2023-01 | |
卷号 | 133期号:109012页码:109012 |
文章类型 | 模式识别,机器学习 |
摘要 | Most deep learning models are elaborately designed for balanced datasets, and thus they inevitably suffer performance degradation in practical long-tailed recognition tasks, especially to the minority classes. There are two crucial issues in learning from imbalanced datasets: skew decision boundary and unrepresentative feature space. In this work, we establish a theoretical framework to analyze the sources of these two issues from Bayesian perspective, and find that they are closely related to the prior gap and the representation gap, respectively. Under this framework, we show that existing long-tailed recognition methods manage to remove either the prior gap or the presentation gap. Different from these methods, we propose to simultaneously remove the two gaps to achieve more accurate long-tailed recognition. Specifically, we propose the prior calibration strategy to remove the prior gap and introduce three strategies (representative feature extraction, optimization strategy adjustment and effective sample modeling) to mitigate the representation gap. Extensive experiments on five benchmark datasets validate the superiority of our method against the state-of-the-art competitors. |
关键词 | Long-tailed learning Prior gap Representation gap Image recognition |
DOI | 10.1016/j.patcog.2022.109012 |
收录类别 | SCI |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/55699 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Zhang Ming-Liang |
作者单位 | 1.National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhang Ming-Liang,Zhang Xu-Yao,Wang Chang,et al. Towards Prior Gap and Representation Gap for Long-tailed Recognition, Pattern Recognition[J]. Pattern Recognition,2023,133(109012):109012. |
APA | Zhang Ming-Liang,Zhang Xu-Yao,Wang Chang,&Liu Cheng-Lin.(2023).Towards Prior Gap and Representation Gap for Long-tailed Recognition, Pattern Recognition.Pattern Recognition,133(109012),109012. |
MLA | Zhang Ming-Liang,et al."Towards Prior Gap and Representation Gap for Long-tailed Recognition, Pattern Recognition".Pattern Recognition 133.109012(2023):109012. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
1-s2.0-S003132032200(2258KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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