Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation
Anomaly detection is an important problem for recent advances in machine learning. To this end, many attempts have emerged to detect unknown anomalies of the images by learning representations and designing score functions. In this paper, we propose a simple yet effective framework for unsupervised...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
IEEE
2021
|
| Subjects: | |
| Online Access: | https://doaj.org/article/c801b504e22a487a82b92f13215e2473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|