AnomalyControl: Few-Shot Anomaly Generation by ControlNet Inpainting
Quality inspection tasks, i.e., anomaly detection, localization and classification, face the scarcity of non-nominal images in real industrial scenarios. Hence, generative models have been explored as a tool to obtain defective images from few real labelled samples. Despite the fast-increasing quali...
Saved in:
| Main Authors: | Musawar Ali, Nicola Fioraio, Samuele Salti, Luigi Di Stefano |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10806704/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Design of 2D-Scanning Bifocal Metalens Antenna Using ControlNet-Enabled Stable Diffusion Deep Generative Model
by: Cedric W. L. Lee, et al.
Published: (2025-01-01) -
Self-diffusion anomalies of an odd tracer in soft-core media
by: Pietro Luigi Muzzeddu, et al.
Published: (2025-01-01) -
Few-Shot Graph Anomaly Detection via Dual-Level Knowledge Distillation
by: Xuan Li, et al.
Published: (2025-01-01) -
Graph-Attention Diffusion for Enhanced Multivariate Time-Series Anomaly Detection
by: Vadim Lanko, et al.
Published: (2024-01-01) -
Wavelet-based diffusion with spatial-frequency attention for hyperspectral anomaly detection
by: Sitian Liu, et al.
Published: (2025-08-01)