论文标题
在预训练的深度特征中建模正常数据的分布以进行异常检测
Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
论文作者
论文摘要
图像中的异常检测（AD）是一个基本的计算机视觉问题，是指识别显着偏离规范的图像和图像子结构。流行的AD算法通常会尝试使用特定于任务数据集从划痕中学习正常性模型，但由于大规模的异常性与异常外观的模棱两可的性质相结合，因此仅限于采用正常数据的半监督方法。 我们遵循另一种方法，并证明，在大型自然图像数据集上通过判别模型学到的深度特征表示非常适合描述正常性，并且在转移学习环境中甚至发现了微妙的异常。我们的正态性模型是通过将多元高斯（MVG）拟合到仅使用正常数据在Imagenet上训练的分类网络的深度特征表示来建立的。通过随后将Mahalanobis距离作为异常得分，我们在公共MVTEC广告数据集上胜过当前最新技术的状态，在所有15个类中，AUROC值$ 95.8 \ pm 1.2 $（平均$ \ pm $ sem）。我们进一步研究了为什么学习的表示形式使用主组件分析对AD任务有歧视。我们发现，正常数据中包含差异很小的主要成分对于区分正常情况和异常实例至关重要。这可能解释了仅使用普通数据从头开始训练的AD方法通常低于标准的性能。通过选择性地将MVG拟合到这些最相关的组件中，我们能够在保留广告性能的同时进一步降低模型复杂性。我们还通过基于MVG假设选择可接受的假阳性率阈值来研究工作点。 可在https://github.com/orippler/gaussian-ad-mvtec上找到代码
Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images and image substructures that deviate significantly from the norm. Popular AD algorithms commonly try to learn a model of normality from scratch using task specific datasets, but are limited to semi-supervised approaches employing mostly normal data due to the inaccessibility of anomalies on a large scale combined with the ambiguous nature of anomaly appearance. We follow an alternative approach and demonstrate that deep feature representations learned by discriminative models on large natural image datasets are well suited to describe normality and detect even subtle anomalies in a transfer learning setting. Our model of normality is established by fitting a multivariate Gaussian (MVG) to deep feature representations of classification networks trained on ImageNet using normal data only. By subsequently applying the Mahalanobis distance as the anomaly score we outperform the current state of the art on the public MVTec AD dataset, achieving an AUROC value of $95.8 \pm 1.2$ (mean $\pm$ SEM) over all 15 classes. We further investigate why the learned representations are discriminative to the AD task using Principal Component Analysis. We find that the principal components containing little variance in normal data are the ones crucial for discriminating between normal and anomalous instances. This gives a possible explanation to the often sub-par performance of AD approaches trained from scratch using normal data only. By selectively fitting a MVG to these most relevant components only, we are able to further reduce model complexity while retaining AD performance. We also investigate setting the working point by selecting acceptable False Positive Rate thresholds based on the MVG assumption. Code available at https://github.com/ORippler/gaussian-ad-mvtec
