DeepAI
Log In Sign Up

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

11/24/2021

Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks

Deep anomaly detection has proven to be an efficient and robust approach...
04/28/2021

PANDA : Perceptually Aware Neural Detection of Anomalies

Semi-supervised methods of anomaly detection have seen substantial advan...
09/05/2022

FIRED: a fine-grained robust performance diagnosis framework for cloud applications

To run a cloud application with the required service quality, operators ...
11/12/2021

Variation and generality in encoding of syntactic anomaly information in sentence embeddings

While sentence anomalies have been applied periodically for testing in N...
08/13/2019

Detecting semantic anomalies

We critically appraise the recent interest in out-of-distribution (OOD) ...