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Intro
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Anomaly Detection: Video Surveillance.
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Anomaly Detection: By Spectral Techniques
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Anomaly Detection: PCA
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Conventional Anomaly Detection Techniques
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Matrix Factorization Approach: PCA
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Auto-encoders for anomaly detection.
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Comparison: Conventional Anomaly Detection Methods
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Robust (convolution) Auto-Encoders RCAE
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RCAE Vs Robust PCA (1)
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Training RCAE (1)
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Summary of Datasets
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Anomaly Detection: Methods Compared
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Experiment Settings
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Methodology
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Non Inductive: Top anomalous Images Detected USPS : 220 images of '1's, and 11 images of 7 (anomalous)
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Non Inductive Anomaly Detection: Performance
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Image De-noising Capability: RCAE vs RPCA
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Conclusion
Description:
Explore robust deep learning methods for anomaly detection in this 24-minute conference talk from KDD 2020. Delve into various techniques, including video surveillance, spectral methods, PCA, and auto-encoders. Learn about matrix factorization approaches and robust convolutional auto-encoders (RCAE). Compare conventional and deep learning-based anomaly detection methods through experiments on diverse datasets. Discover the performance of non-inductive anomaly detection and image de-noising capabilities of RCAE versus RPCA. Gain insights from speakers Raghavendra Chalapathy, Khoa Nguyen, and Sanjay Chawla as they present their findings and conclusions on advanced anomaly detection techniques.

KDD 2020: Robust Deep Learning Methods for Anomaly Detection

Association for Computing Machinery (ACM)
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#Data Science #Data Analysis #Anomaly Detection #Computer Science #Machine Learning #Deep Learning #Matrix Factorization
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Intro