.png){kind=icon}
返回
One-Class Classification Using Generative Adversarial Networks
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2905933.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One-class classification (OCC) problem has drawn increasing attention in recent years. It expands the range of classification from pre-defined categories to undefined categories. Since the vast diversity of negative samples, it is hard to acquire complete knowledge of unknown classes and construct a negative set for training a one-class classifier, which remains a difficult problem. In this paper, we propose a new OCC model by modifying the generative adversarial network model to address the OCC problem. Taking the generator's outputs as outliers, the discriminator in our model is trained with these synthetic data and target training data, and it manages to distinguish them from each other. Moreover, a new evaluation protocol named classification recall index is put forward to indicate the classifier's performances on both positive and negative sets. The extensive experiments on the MNIST dataset and the Street View House Numbers (SVHN) dataset demonstrate that the proposed model is competitive over a variety of OCC methods.
Keyword:
Generative adversarial networks
one-class
image classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容