gan中损坏数据的正无标记分类

IF 18.6
Yunke Wang;Chang Xu;Tianyu Guo;Bo Du;Dacheng Tao
{"title":"gan中损坏数据的正无标记分类","authors":"Yunke Wang;Chang Xu;Tianyu Guo;Bo Du;Dacheng Tao","doi":"10.1109/TPAMI.2025.3565394","DOIUrl":null,"url":null,"abstract":"This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs and deal with corrupted data. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods. Considering the potential corrupted data problem in real-world scenarios, we further extend our approach to PUGAN-C, which treats real data as unlabeled that accounts for both clean and corrupted instances, and generated data as positive. The samples from generator could be closer to those corrupted data within unlabeled data at first, but within the framework of adversarial training, the generator will be optimized to cheat the discriminator and produce samples that are similar to those clean data. Experimental results on image generation from several corrupted datasets demonstrate the effectiveness and generalization of PUGAN-C.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"47 8","pages":"6859-6875"},"PeriodicalIF":18.6000,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On Positive-Unlabeled Classification From Corrupted Data in GANs\",\"authors\":\"Yunke Wang;Chang Xu;Tianyu Guo;Bo Du;Dacheng Tao\",\"doi\":\"10.1109/TPAMI.2025.3565394\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs and deal with corrupted data. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods. Considering the potential corrupted data problem in real-world scenarios, we further extend our approach to PUGAN-C, which treats real data as unlabeled that accounts for both clean and corrupted instances, and generated data as positive. The samples from generator could be closer to those corrupted data within unlabeled data at first, but within the framework of adversarial training, the generator will be optimized to cheat the discriminator and produce samples that are similar to those clean data. Experimental results on image generation from several corrupted datasets demonstrate the effectiveness and generalization of PUGAN-C.\",\"PeriodicalId\":94034,\"journal\":{\"name\":\"IEEE transactions on pattern analysis and machine intelligence\",\"volume\":\"47 8\",\"pages\":\"6859-6875\"},\"PeriodicalIF\":18.6000,\"publicationDate\":\"2025-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on pattern analysis and machine intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10980032/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on pattern analysis and machine intelligence","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10980032/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文定义了一个标准gan的正无标记分类问题,从而提出了一种稳定gan中判别器训练和处理损坏数据的新技术。传统上,真实数据为正,生成数据为负。这个正反分类标准在判别器的整个学习过程中都是固定不变的,而没有考虑到生成的数据质量的逐渐提高,即使它们有时比真实数据更真实。相比之下,将生成的数据视为未标记的数据更为合理,根据其质量可以是正的,也可以是负的。因此,判别器是这种正无标记分类问题的分类器,我们推导了一种新的正无标记GAN (PUGAN)。从理论上讨论了该模型所能达到的全局最优性和等效的优化目标。经验表明,PUGAN可以达到与那些复杂的鉴别器稳定方法相当甚至更好的性能。考虑到现实场景中潜在的损坏数据问题,我们进一步将我们的方法扩展到PUGAN-C,它将真实数据视为未标记的,说明了干净和损坏的实例,并将生成的数据视为正数据。首先,生成器的样本可能更接近未标记数据中的损坏数据,但在对抗训练的框架内,生成器将被优化以欺骗鉴别器并产生与那些干净数据相似的样本。在多个损坏数据集上生成图像的实验结果证明了PUGAN-C算法的有效性和通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Positive-Unlabeled Classification From Corrupted Data in GANs
This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs and deal with corrupted data. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept fixed all through the learning process of the discriminator without considering the gradually improved quality of generated data, even if they could be more realistic than real data at times. In contrast, it is more reasonable to treat the generated data as unlabeled, which could be positive or negative according to their quality. The discriminator is thus a classifier for this positive and unlabeled classification problem, and we derive a new Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality the proposed model will achieve and the equivalent optimization goal. Empirically, we find that PUGAN can achieve comparable or even better performance than those sophisticated discriminator stabilization methods. Considering the potential corrupted data problem in real-world scenarios, we further extend our approach to PUGAN-C, which treats real data as unlabeled that accounts for both clean and corrupted instances, and generated data as positive. The samples from generator could be closer to those corrupted data within unlabeled data at first, but within the framework of adversarial training, the generator will be optimized to cheat the discriminator and produce samples that are similar to those clean data. Experimental results on image generation from several corrupted datasets demonstrate the effectiveness and generalization of PUGAN-C.
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
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信