Augmented Time Regularized Generative Adversarial Network (ATR-GAN) for Data Augmentation in Online Process Anomaly Detection

Li, Yuxuan; Shi, Zhangyue; Liu, Chenang; Tian, Wenmeng; Kong, Zhenyu; Williams, Christopher

doi:10.1109/tase.2021.3118635

Cited by 38 publications

(5 citation statements)

References 51 publications

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“…Sabokrou et al [1] attempt to de-noise noisy samples of the given class, and the discriminator's prediction in the image space is used to quantify the reconstruction error. Li et al [19] propose an augmented time-regularized generative adversarial network to generate effective artificial samples for novelty detection. Chen et al [20] leverage an encoder-decoder reconstruction network and a CNN-based discrimination network to recognize noisy novel samples.…”

Section: Related Workmentioning

confidence: 99%

Complex Background Reconstruction for Novelty Detection

Zhao,

Su,

et al. 2023

Applied Sciences

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Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Complex Background Reconstruction for Novelty Detection

Zhao,

Su,

et al. 2023

Applied Sciences

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“…To solve this problem and increase the efficiency of training, data generation using GANs comes to the rescue [3]- [5]. This paper focuses on two important methods within GANs: pixel-to-pixel (Pix2Pix) [6], [7] and regular GAN [8]- [10]. Pix2Pix, which is based on the idea of learning from paired data before and after transformation, is a powerful tool for creating high-quality images [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Data generation using generative adversarial networks to increase data volume

Aitimova,

Aitimov,

Mukhametzhanova

et al. 2024

IJECE

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The article is an in-depth analysis of two leading approaches in the field of generative modeling: generative adversarial networks (GANs) and the pixel-to-pixel (Pix2Pix) image translation model. Given the growing interest in automation and improved image processing, the authors focus on the key operating principles of each model, analyzing their unique characteristics and features. The article also explores in detail the various applications of these approaches, highlighting their impact on modern research in computer vision and artificial intelligence. The purpose of the study is to provide readers with a scientific understanding of the effectiveness and potential of each of the models, and to highlight the opportunities and limitations of their application. The authors strive not only to cover the technical aspects of the models, but also to provide a broad overview of their impact on various industries, including medicine, the arts, and solving real-world problems in image processing. In addition, we have identified prospects for the use of these technologies in various fields, such as medicine, design, art, entertainment, and in unmanned aerial vehicle systems. The ability of GANs and Pix2Pix to adapt to a variety of tasks and produce high-quality results opens up broad prospects for industry and research.

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“…In general, acquiring OOD samples is expensive [4]; thus, anomaly detection methods that use only ID samples are primarily employed. These methods include defining a closed one-class distribution using support vectors, where samples outside the distribution are detected as outliers [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection via a Bottleneck Structure Robust to Noise in In-Distribution Samples

Cho,

Lee,

Hyun

et al. 2024

IEEE Access

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Anomaly detection aims to distinguish data that are not part of in-distribution (ID) samples. It detects unknown defect images considered out-of-distribution (OOD) samples using only existing defect images in manufacturing. Recently, networks pretrained on large datasets, such as ImageNet, have been used to extract ID features. These methods use the distance between the extracted features of the testing sample and all the training samples as an anomaly score. This approach identifies anomaly samples simply and effectively without requiring additional training. However, the extracted feature is high dimensional; thus, it contains considerable unnecessary information, resulting in a sensitivity to noise in some ID samples. In addition, during the nearest-neighbor search process, false positive occurs as a result of an ID sample with a low probability of occurrence. This paper presents a feature extraction methodology that is robust to noise by adopting a bottleneck structure used in reverse knowledge distillation to solve the noise sensitivity problem. Next, the false positive was solved using K-means clustering-based augmentation for samples far from each cluster center. Finally, we propose a weighted scoring function that increases the anomaly score when the nearest ID samples from the testing sample are rare. Applying the proposed methodology to the electric vehicle battery dataset and CIFAR10 revealed that the performance improved compared to the baseline model.

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Augmented Time Regularized Generative Adversarial Network (ATR-GAN) for Data Augmentation in Online Process Anomaly Detection

Cited by 38 publications

References 51 publications

Complex Background Reconstruction for Novelty Detection

Complex Background Reconstruction for Novelty Detection

Data generation using generative adversarial networks to increase data volume

Anomaly Detection via a Bottleneck Structure Robust to Noise in In-Distribution Samples

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