  Detecting anomaly patterns from images is a crucial artificial intelligence
technique in industrial applications. Recent research in this domain has
emphasized the necessity of a large volume of training data, overlooking the
practical scenario where, post-deployment of the model, unlabeled data
containing both normal and abnormal samples can be utilized to enhance the
model's performance. Consequently, this paper focuses on addressing the
challenging yet practical few-shot online anomaly detection and segmentation
(FOADS) task. Under the FOADS framework, models are trained on a few-shot
normal dataset, followed by inspection and improvement of their capabilities by
leveraging unlabeled streaming data containing both normal and abnormal samples
simultaneously.
  To tackle this issue, we propose modeling the feature distribution of normal
images using a Neural Gas network, which offers the flexibility to adapt the
topology structure to identify outliers in the data flow. In order to achieve
improved performance with limited training samples, we employ multi-scale
feature embedding extracted from a CNN pre-trained on ImageNet to obtain a
robust representation. Furthermore, we introduce an algorithm that can
incrementally update parameters without the need to store previous samples.
Comprehensive experimental results demonstrate that our method can achieve
substantial performance under the FOADS setting, while ensuring that the time
complexity remains within an acceptable range on MVTec AD and BTAD datasets.
