Purpose

In novelty detection, the autoencoder based image reconstruction strategy is one of the mainstream solutions. The basic idea is that once the autoencoder is trained on normal data, it has a low reconstruction error on normal data. However, when faced with complex natural images, the conventional pixel-level reconstruction becomes poor and does not show the promising results. This paper aims to provide a new method for improving the performance of novelty detection based autoencoder.

Design/methodology/approach

To solve the problem that conventional pixel-level reconstruction cannot effectively extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global semantic feature information. Then, the channel attention mechanism is introduced to perform adaptive feature refinement on the intermediate feature map to optimize the correspondingly passed feature map.

Findings

Experimental results on three public data sets show that the proposed method has potential performance for novelty detection.

Originality/value

This study explores the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images. In this way, the performance of novelty detection can be improved.

中文翻译：

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通过自监督学习和通道注意机制改进新颖性检测

目的

在新颖性检测中，基于自动编码器的图像重建策略是主流解决方案之一。基本思想是，一旦自动编码器在正常数据上进行训练，它在正常数据上的重构误差就很低。然而，当面对复杂的自然图像时，传统的像素级重建变得很差，并没有显示出有希望的结果。本文旨在提供一种提高基于新颖性检测的自编码器性能的新方法。

设计/方法/方法

针对传统像素级重建无法有效提取图像全局语义信息的问题，提出了一种将注意力机制与自监督学习方法相结合的新模型。首先，设置一个辅助任务，重建旋转图像，使网络能够学习全局语义特征信息。然后，引入通道注意力机制，对中间特征图进行自适应特征细化，优化对应通过的特征图。

发现

在三个公共数据集上的实验结果表明，所提出的方法具有潜在的新颖性检测性能。

原创性/价值

本研究探讨了自监督学习方法和注意机制在单一类别图像上提取特征的能力。通过这种方式，可以提高新颖性检测的性能。