In industrial scenarios, it is crucial not only to identify anomalous items
but also to classify the type of anomaly. However, research on anomaly
multi-classification remains largely unexplored. This paper proposes a novel
and valuable research task called anomaly multi-classification. Given the
challenges in applying few-shot learning to this task, due to limited training
data and unique characteristics of anomaly images, we introduce a baseline
model that combines RelationNet and PatchCore. We propose a data generation
method that creates pseudo classes and a corresponding proxy task, aiming to
bridge the gap in transferring few-shot learning to industrial scenarios.
Furthermore, we utilize contrastive learning to improve the vanilla baseline,
achieving much better performance than directly fine-tune a ResNet. Experiments
conducted on MvTec AD and MvTec3D AD demonstrate that our approach shows
superior performance in this novel task.

本文提出了一个名为异常多分类的新颖而有价值的研究任务，并介绍了一种结合了 RelationNet 和 PatchCore 的基线模型，以及一种数据生成方法和对比学习，旨在在工业场景中实现将 few-shot learning 转化的性能改进。在 MvTec AD 和 MvTec3D AD 上的实验证明了我们方法在这个新任务中表现出优越的性能。

工业场景中的异常多分类：将 Few-shot Learning 转移到新任务中

Anomaly Multi-classification in Industrial Scenarios: Transferring  Few-shot Learning to a New Task

Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build systems that work well simultaneously on many different tasks
automatically. The best performing approaches combine embeddings from ImageNet
models with an outlier detection model. In this paper, we extend on this line
of work and propose \textbf{PatchCore}, which uses a maximally representative
memory bank of nominal patch-features. PatchCore offers competitive inference
times while achieving state-of-the-art performance for both detection and
localization. On the challenging, widely used MVTec AD benchmark PatchCore
achieves an image-level anomaly detection AUROC score of up to $99.6\%$, more
than halving the error compared to the next best competitor. We further report
competitive results on two additional datasets and also find competitive
results in the few samples regime.\freefootnote{$^*$ Work done during a
research internship at Amazon AWS.} Code:
github.com/amazon-research/patchcore-inspection.

本文提出了一种名为 PatchCore 的方法，它使用最具代表性的内存库来实现商标标准化的图像检测和本地化。PatchCore 在 MVTec AD 挑战赛中实现了高达 99.6％的图像级异常检测 AUROC 分数，并在其他两个数据集中取得了竞争性结果。