Case studies in hardware XPath acceleration
Dorit Nuzman, David Maze, et al.
SYSTOR 2011
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.
Dorit Nuzman, David Maze, et al.
SYSTOR 2011
Xiao-Yu Hu, Evangelos Eleftheriou, et al.
Israeli SYSTOR 2009
Xiaohui Shen, Gang Hua, et al.
FG 2011
Bogdan Prisacari, German Rodriguez, et al.
INA-OCMC 2014