论文速读:Deep Anomaly Detection Using Geometric Transformations [2018 NPIS]
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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相关文章
- 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》