Normalizing flows for deep anomaly detection

12/19/2019
by   Artem Ryzhikov, et al.
31

Anomaly detection for complex data is a challenging task from the perspective of machine learning. In this work, weconsider cases with missing certain kinds of anomalies in the training dataset, while significant statistics for the normal class isavailable. For such scenarios, conventional supervised methods might suffer from the class imbalance, while unsupervised methodstend to ignore difficult anomalous examples. We extend the idea of the supervised classification approach for class-imbalanceddatasets by exploiting normalizing flows for proper Bayesian inference of the posterior probabilities.

READ FULL TEXT

page 3

page 7

research
01/12/2021

Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators

Anomaly detection is a challenging task for machine learning algorithms ...
research
05/17/2023

Reconstruction Error-based Anomaly Detection with Few Outlying Examples

Reconstruction error-based neural architectures constitute a classical d...
research
02/13/2023

Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics

One-class classification has been a prevailing method in building deep a...
research
10/19/2020

Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning

Deep anomaly detection models using a supervised mode of learning usuall...
research
02/22/2019

Bayesian Anomaly Detection and Classification

Statistical uncertainties are rarely incorporated in machine learning al...
research
02/11/2019

Scaling Up Anomaly Detection Using In-DRAM Working Set of Active Flows Table

In the zettabyte era, per-flow measurement becomes more challenging owin...
research
03/29/2022

Radial Autoencoders for Enhanced Anomaly Detection

In classification problems, supervised machine-learning methods outperfo...

Please sign up or login with your details

Forgot password? Click here to reset