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  • 论文阅读 WACV2021 Same Same But DifferNet: Semi . . .
    缺陷检测（defect detection）是 异常检测 （anomaly detection）的特殊 子问题，并且定义异常检测的模型：数据样本是否不同于给定 正常样本的集合 （whether a data sample differs from a set of given normal data） 异常检测方法： 基于训练的模型：如OCSVM,1-NN等。 基于生成式模型：如GAN， 流模型。 其中生成模型适用于很大范围的缺陷检测方法，只需较少的样本。 使用正常样本训练概率分布，那么异常样本就是分布外，因此有较低的 似然值。 （参考One Class SVM的思想）
  • Same Same But DifferNet: Semi-Supervised Defect Detection with . . .
    To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows Normalizing flows are well-suited to deal with low dimensional data distributions However, they struggle with the high dimensionality of images
  • This is the official repository to the WACV 2021 paper Same Same But . . .
    This is the official repository to the WACV 2021 paper "Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows" by Marco Rudolph, Bastian Wandt and Bodo Rosenhahn
  • Same Same but DifferNet: Semi-Supervised Defect Detection With . . .
    similar normal samples and only slightly different anoma-lous samples are present While traditional anomaly detec-tion methods are well-suited to data with high intra-class variance, they are not able to capture subtle differences We tackle this problem by employing an accurate density esti-mator on image features extracted by a convolutional
  • 异常检测小结：Density_based Normalizing Flow_same . . .
    在近一两年发表的论文里，DifferNet网络(Marco Rudolph, Wandt, Rosenhahn, 2021)是基于深度学习的图像特征密度估计方法，将标准化流引入异常检测领域，但该网络由于只利用生成的最后特征层，缺少重要的上下文信息和语义信息，通过数据增强的方法解决
  • Same Same But DifferNet: Semi-Supervised Defect Detection with . . .
    Since many defects occur very rarely and their characteristics are mostly unknown a priori, their detection is still an open research question To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows
  • DifferNet Explained - Papers With Code
    DifferNet Introduced by Rudolph et al in Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
  • Same Same But DifferNet: Semi-Supervised Defect Detection with . . .
    We presented DifferNet to detect defects in images by utilizing a normalizing-flow-based density estimation of image features at multiple scales Likelihoods of several transformations of a single image are used to compute a robust anomaly score
  • DifferNet【异常检测：Normalizing Flow】 - CSDN博客
    为了缓解这一问题并推进无监督视觉检测的最新技术，这项工作提出了一个基于DifferNet的解决方案，增强了注意力模块：AttentDifferNet。 它在三个工业检测的视觉 异常检测 数据集上提高了图像级检测和分类能力：InsPLAD-fault、MVTec AD和半导体晶圆。
  • WACV 2021 Open Access Repository
    To this end, we propose DifferNet: It leverages the descriptiveness of features extracted by convolutional neural networks to estimate their density using normalizing flows Normalizing flows are well-suited to deal with low dimensional data distributions However, they struggle with the high dimensionality of images





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