KDD 2020: Robust Deep Learning Methods for Anomaly Detection

KDD 2020: Robust Deep Learning Methods for Anomaly Detection

Association for Computing Machinery (ACM) via YouTube Direct link

Matrix Factorization Approach: PCA

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6 of 19

Matrix Factorization Approach: PCA

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KDD 2020: Robust Deep Learning Methods for Anomaly Detection

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  1. 1 Intro
  2. 2 Anomaly Detection: Video Surveillance.
  3. 3 Anomaly Detection: By Spectral Techniques
  4. 4 Anomaly Detection: PCA
  5. 5 Conventional Anomaly Detection Techniques
  6. 6 Matrix Factorization Approach: PCA
  7. 7 Auto-encoders for anomaly detection.
  8. 8 Comparison: Conventional Anomaly Detection Methods
  9. 9 Robust (convolution) Auto-Encoders RCAE
  10. 10 RCAE Vs Robust PCA (1)
  11. 11 Training RCAE (1)
  12. 12 Summary of Datasets
  13. 13 Anomaly Detection: Methods Compared
  14. 14 Experiment Settings
  15. 15 Methodology
  16. 16 Non Inductive: Top anomalous Images Detected USPS : 220 images of '1's, and 11 images of 7 (anomalous)
  17. 17 Non Inductive Anomaly Detection: Performance
  18. 18 Image De-noising Capability: RCAE vs RPCA
  19. 19 Conclusion

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