It may result in blurry output and large reconstruction error even for normal samples. However, due to the existence of down-sampling in AE, the latent coding representation loses the detailed information of the original image. It is generally assumed that the reconstruction error of the normal samples which are similar to the training data will be lower than abnormal samples. In particular, autoencoder (AE) is a natural choice to model the high-dimensional image data in the unsupervised setting. Most recent methods are based on convolutional neural networks (CNNs). Reconstruction-based methods have been widely used for unsupervised anomaly detection. Remarkably, we improve the vanilla autoencoder model by 10.1 We achieve state-of-the-art performance on the challenging MVTec ADĭataset. Representation of the discriminator, which improves the detection of subtleĪnomaly. Novel multi-scale block-wise memory module into an autoencoder network.īesides, we introduce adversarial learning and explore the semantic latent
The divide-and-assemble framework is implemented by embedding a The gap between the reconstruction error of normal and abnormal samples can be That is, finer granularity leads to better reconstruction, whileĬoarser granularity leads to poorer reconstruction. The reconstruction capability of the model for both normal and abnormal Varying the granularity of division on feature maps, we are able to modulate
Reconstruction of an image as a divide-and-assemble procedure. Models such as autoencoder do not work well. Generalizability of deep neural networks is difficult to control, existing Samples and poor reconstruction for abnormal samples. Ideally, we expect a perfect reconstruction for normal Reconstruction-based methods play an important role in unsupervised anomalyĭetection in images.
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