Anomaly detection has a wide range of applications and is especially
important in industrial quality inspection. Currently, many top-performing
anomaly-detection models rely on feature-embedding methods. However, these
methods do not perform well on datasets with large variations in object
locations. Reconstruction-based methods use reconstruction errors to detect
anomalies without considering positional differences between samples. In this
study, a reconstruction-based method using the noise-to-norm paradigm is
proposed, which avoids the invariant reconstruction of anomalous regions. Our
reconstruction network is based on M-net and incorporates multiscale fusion and
residual attention modules to enable end-to-end anomaly detection and
localization. Experiments demonstrate that the method is effective in
reconstructing anomalous regions into normal patterns and achieving accurate
anomaly detection and localization. On the MPDD and VisA datasets, our proposed
method achieved more competitive results than the latest methods, and it set a
new state-of-the-art standard on the MPDD dataset.

提出了一种基于重建的方法，利用噪声到规范范式来检测工业质量检测中的异常，通过多尺度融合和残差注意力模块实现端到端的异常检测和定位，在 MPDD 和 VisA 数据集上取得了比最新方法更有竞争力的结果，并在 MPDD 数据集上创造了新的最新标准。

工业异常检测与定位的噪声到规范重构

Noise-to-Norm Reconstruction for Industrial Anomaly Detection and  Localization

Reading text in the wild is a challenging task in the field of computer
vision. Existing approaches mainly adopted Connectionist Temporal
Classification (CTC) or Attention models based on Recurrent Neural Network
(RNN), which is computationally expensive and hard to train. In this paper, we
present an end-to-end Attention Convolutional Network for scene text
recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers
to effectively capture the contextual dependencies of the input sequence, which
is characterized by lower computational complexity and easier parallel
computation. Compared to the chain structure of recurrent networks, the
Convolutional Neural Network (CNN) provides a natural way to capture long-term
dependencies between elements, which is 9 times faster than Bidirectional Long
Short-Term Memory (BLSTM). Furthermore, in order to enhance the representation
of foreground text and suppress the background noise, we incorporate the
residual attention modules into a small densely connected network to improve
the discriminability of CNN features. We validate the performance of our
approach on the standard benchmarks, including the Street View Text, IIIT5K and
ICDAR datasets. As a result, state-of-the-art or highly-competitive performance
and efficiency show the superiority of the proposed approach.

本文提出了基于注意力卷积网络的端到端场景文本识别方法，通过卷积神经网络 (CNN) 代替循环神经网络 (RNN) 来实现输入序列的上下文依赖关系的准确捕捉，提高了识别效率，并结合残余注意力模块进一步提高特征识别的准确性。该方法在多个数据集上验证结果显示了显著的性能优势。