传统的无监督异常检测(Unsupervised Anomaly Detection, UAD)方法通常假设正常样本具有一致的模式,而异常样本则偏离这些模式。然而,在多类异常检测中,正常样本可能来自多个不同的类别,具有多样化的模式。这种多样性可能导致模型在面对未见过的模式时,倾向于将其泛化为正常样本,从而无法有效检测异常。这种现象被称为
尽管扩散模型在生成高质量样本方面表现卓越,但其设计初衷是从纯噪声生成图像。因此直接应用于修改输入图像需经历前向扩散与反向去噪过程,这一策略存在多重局限: 首先,在多类别场景中直接使用DDPM可能导致生成图像因原始类别信息丢失而被误分类[8](当采用高步数扩散时);反之若减少扩散步数,则会出现"身份捷径"问题——...
Unsupervised anomaly detection techniques, which do not rely on prior knowledge of anomalies, have attracted considerable attention in the field of industrial surface inspection. However, existing approaches commonly employ separate models for each product class, resulting in substantial storage requirements ...
have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring Locality-Enhanced State Space (LSS) modules at multi-scales. The proposed LSS modu...
One-class classification is an unsupervised learning task where only one of the classes is utilized and other instances are ignored. It is also known as novelty detection, anomaly detection, data description, and concept learning (Tax, 2001). A one-class classifier, trained by a one-class clas...
Cardama FJ, Heras DB, Argüello F (2023) Consensus techniques for unsupervised binary change detection using multi-scale segmentation detectors for land cover vegetation images. Remote Sens 15(11):2889 Article Google Scholar Chaitanya P, Pranaya D, Israel S, Arun M, Kanak M, Subodh CP, Fahad...
Pandarachalil R, Selvaraju S, GS M (2014) Twitter sentiment analysis for large-scale data: an unsupervised approach. Cognit Comput 7:254–262. https://doi.org/10.1007/s12559-014-9310-z Article Google Scholar Parveen H, Pandey S (2016) Sentiment analysis on twitter data-set using naive ...
The authors construct a pseudo-labeled support/query pair based on one unlabeled image slice and its unsupervised superpixel segmentation. The support label is then generated by randomly selecting a superpixel from the support image’s superpixel segmentation and binarizing it to obtain a binary mask...
The network is trained in an unsupervised way to minimize the reconstruction error. An AE can be thought of as a generalization of the PCA approach which can find a non-linear manifold assuming non-linear activation functions. Many different types of AEs have been proposed including sparse auto...
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection ruiyinglu/hvq-trans • • NeurIPS 2023 First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector ...