Fine-grained Anomaly Detection via Multi-task Self-Supervision

04/20/2021
by   Loic Jezequel, et al.
0

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining in a multi-task framework high-scale shape features oriented task with low-scale fine features oriented task, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31 with AUROC on various anomaly detection problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/24/2021

Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks

Deep anomaly detection has proven to be an efficient and robust approach...
research
06/14/2023

SaliencyCut: Augmenting Plausible Anomalies for Open-set Fine-Grained Anomaly Detection

Open-set fine-grained anomaly detection is a challenging task that requi...
research
08/11/2022

Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning

Fine-Grained Change Detection and Regression Analysis are essential in m...
research
02/23/2023

Set Features for Fine-grained Anomaly Detection

Fine-grained anomaly detection has recently been dominated by segmentati...
research
07/14/2020

ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level

Previous works on the CERT insider threat detection case have neglected ...
research
09/26/2019

RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods

Decision-tree-based ensemble classification methods (DTEMs) are a preval...
research
02/28/2023

Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection

Medical anomalous data normally contains fine-grained instance-wise addi...

Please sign up or login with your details

Forgot password? Click here to reset