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.

该论文研究了在工业应用中从图像中检测异常模式的关键人工智能技术，提出了一种少样本在线异常检测和分割的框架，通过利用包含正常和异常样本的无标签数据来提高模型性能，使用神经气体网络对正常图像的特征分布进行建模，采用从预训练的 ImageNet 中提取的多尺度特征嵌入来获得鲁棒的表示，同时引入一种能够增量更新参数而无需存储先前样本的算法。实验结果表明，该方法在 FOADS 设置下能够取得显著的性能，并确保时间复杂度在可接受范围内。

少样本在线异常检测和分割

Few-shot Online Anomaly Detection and Segmentation

The ability to incrementally learn new classes is crucial to the development
of real-world artificial intelligence systems. In this paper, we focus on a
challenging but practical few-shot class-incremental learning (FSCIL) problem.
FSCIL requires CNN models to incrementally learn new classes from very few
labelled samples, without forgetting the previously learned ones. To address
this problem, we represent the knowledge using a neural gas (NG) network, which
can learn and preserve the topology of the feature manifold formed by different
classes. On this basis, we propose the TOpology-Preserving knowledge
InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old
classes by stabilizing NG's topology and improves the representation learning
for few-shot new classes by growing and adapting NG to new training samples.
Comprehensive experimental results demonstrate that our proposed method
significantly outperforms other state-of-the-art class-incremental learning
methods on CIFAR100, miniImageNet, and CUB200 datasets.

该研究重点关注 CNN 模型在少数有标记样本的情况下如何对新类别进行增量式学习，并提出了基于神经气网络的知识表示和 TOpology-Preserving knowledge InCrementer (TOPIC) 框架来解决这一问题，实验结果表明该方法在几个数据集上优于当前最先进的类增量学习方法。