论文阅读笔记《GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training》
核心思想 本文提出一种基于半监督GAN的异常检测算法，主要的创新点在于在自动编码器之后，又增加了一个编码器，构成了“编码-解码-编码”的结构，通过比较第二个编码器的输出和第一个编码器的输出之间的差别，来评估输入图像是否属于异常图像。网络的训练过程同样也引入了对抗训练的思想，即使用一个区分器对解码器输出的重构图像进行判断，是来自原始图像还是重构图像，整个网络的结构如下图所示 整个网络可以分为
相关文章
- 1. GAN异常检测论文笔记《GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training》
- 2. 【论文】Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
- 3. [论文阅读笔记]Learning Memory-guided Normality for Anomaly Detection
- 4. 论文阅读：Dehaze-GLCGAN: Unpaired Single Image Dehazing Via Adversarial Training
- 5. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 6. 论文阅读笔记《Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity》
- 7. 《Long Text Generation via Adversarial Training with Leaked Information》阅读笔记
- 8. VITAL: Visual Tracking via Adversarial Learning 阅读笔记
- 9. 论文阅读笔记： 2019 ICCV 《Improving Adversarial Robustness via Guided Complement Entropy》
- 10. 【论文阅读】Illuminating Pedestrians via Simultaneous Detection & Segmentation
- 更多相关文章...
- • RSS 阅读器 - RSS 教程
- • C# 文本文件的读写 - C#教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • JDK13 GA发布：5大特性解读
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. GAN异常检测论文笔记《GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training》
- 2. 【论文】Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
- 3. [论文阅读笔记]Learning Memory-guided Normality for Anomaly Detection
- 4. 论文阅读：Dehaze-GLCGAN: Unpaired Single Image Dehazing Via Adversarial Training
- 5. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 6. 论文阅读笔记《Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity》
- 7. 《Long Text Generation via Adversarial Training with Leaked Information》阅读笔记
- 8. VITAL: Visual Tracking via Adversarial Learning 阅读笔记
- 9. 论文阅读笔记： 2019 ICCV 《Improving Adversarial Robustness via Guided Complement Entropy》
- 10. 【论文阅读】Illuminating Pedestrians via Simultaneous Detection & Segmentation