論文速讀: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，想法也很簡單，即data augmentation 運用。 是通過geometric transformation 將one-class 的normal data轉成 multi-class,用標準的cross entropy 訓練分類器，進行分類取得的結果就很驚豔。 整體可分成三部分： 1.
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相關文章
- 1. 論文速讀:Attribute Restoration Framework for Anomaly Detection
- 2. 【論文速讀】DEEP SEMI-SUPERVISED ANOMALY DETECTION
- 3. Deep Anomaly Detection with Deviation Networks 論文筆記
- 4. 論文速讀:Adversarially Learned One-Class Classifier for Novelty Detection [CVPR 2018]
- 5. Face Detection using Deep Learning: An Improved Faster RCNN Approach論文解讀
- 6. 論文解讀Face Detection using Deep Learning: An Improved Faster R-CNN Approach
- 7. 論文筆記：Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
- 8. 論文筆記-AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based
- 9. 論文譯文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 10. 論文閱讀《Edge Detection Using Convolutional Neural Network》