  Anomaly detection deals with detecting deviations from established patterns
within data. It has various applications like autonomous driving, predictive
maintenance, and medical diagnosis. To improve anomaly detection accuracy,
transfer learning can be applied to large, pre-trained models and adapt them to
the specific application context. In this paper, we propose a novel framework
for online-adaptive anomaly detection using transfer learning. The approach
adapts to different environments by selecting visually similar training images
and online fitting a normality model to EfficientNet features extracted from
the training subset. Anomaly detection is then performed by computing the
Mahalanobis distance between the normality model and the test image features.
Different similarity measures (SIFT/FLANN, Cosine) and normality models (MVG,
OCSVM) are employed and compared with each other. We evaluate the approach on
different anomaly detection benchmarks and data collected in controlled
laboratory settings. Experimental results showcase a detection accuracy
exceeding 0.975, outperforming the state-of-the-art ET-NET approach.
