Conference paper
Managing cloned variants: A framework and experience
Julia Rubin, Krzysztof Czarnecki, et al.
SPLC 2013
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to reconstruct compared with normal regions. MAEDAY is the first image-reconstruction-based anomaly detection method that utilizes a pre-trained model, enabling its use for Few-Shot Anomaly Detection (FSAD). We also show the same method works surprisingly well for the novel tasks of Zero-Shot AD (ZSAD) and Zero-Shot Foreign Object Detection (ZSFOD), where no normal samples are available.
Julia Rubin, Krzysztof Czarnecki, et al.
SPLC 2013
Cen Rao, Alexei Gritai, et al.
ICCV 2003
Ba Tu Truong, Svetha Venkatesh, et al.
ICPR 2002
Jun Xie, Rogerio Schmidt Feris, et al.
ICIP 2014