论文速读:Deep Anomaly Detection Using Geometric Transformations [2018 NPIS]

时间 2020-12-25

标签异常侦测 abnormal detection 深度学习 tensorflow 神经网络计算机视觉栏目快乐工作繁體版

原文原文链接

本篇是近期在classification-based 在anomaly detection 上有重大突破，想法也很简单，即data augmentation 运用。是通过geometric transformation 将one-class 的normal data转成 multi-class,用标准的cross entropy 训练分类器，进行分类取得的结果就很惊艳。整体可分成三部分： 1.

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1. 论文速读:Attribute Restoration Framework for Anomaly Detection
2. 【论文速读】DEEP SEMI-SUPERVISED ANOMALY DETECTION
3. Deep Anomaly Detection with Deviation Networks 论文笔记
4. 论文速读:Adversarially Learned One-Class Classifier for Novelty Detection [CVPR 2018]
5. Face Detection using Deep Learning: An Improved Faster RCNN Approach论文解读
6. 论文解读Face Detection using Deep Learning: An Improved Faster R-CNN Approach
7. 论文笔记：Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
8. 论文笔记-AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based
9. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
10. 论文阅读《Edge Detection Using Convolutional Neural Network》

>>更多相关文章<<