Anomaly generation has been widely explored to address the scarcity of anomaly images in real-world data. However, existing methods typically suffer from at least one of the following limitations, hindering their practical deployment: (1) lack of visual realism in generated anomalies; (2) dependence on large amounts of real images; and (3) use of memory-intensive, heavyweight model architectures. To overcome these limitations, we propose AnoStyler, a lightweight yet effective method that frames zero-shot anomaly generation as text-guided style transfer. Given a single normal image along with its category label and expected defect type, an anomaly mask indicating the localized anomaly regions and two-class text prompts representing the normal and anomaly states are generated using generalizable category-agnostic procedures. A lightweight U-Net model trained with CLIP-based loss functions is used to stylize the normal image into a visually realistic anomaly image, where anomalies are localized by the anomaly mask and semantically aligned with the text prompts. Extensive experiments on the MVTec-AD and VisA datasets show that AnoStyler outperforms existing anomaly generation methods in generating high-quality and diverse anomaly images. Furthermore, using these generated anomalies helps enhance anomaly detection performance.

In the rapidly evolving landscape of Industry 4.0 and the forthcoming Industry 5.0, the integration of intelligent systems into industrial automation has become a cornerstone for achieving efficiency, adaptability, and sustainability. In particular, deep learning (DL) has been central to this transformation, by empowering machines to extract meaningful patterns from complex, high-dimensional data, DL has demonstrated remarkable success in tackling industrial challenges such as anomaly detection, predictive maintenance, quality control, and process optimization However, as industrial systems become increasingly interconnected and dynamic, classic supervised learning paradigms often fail to meet the practical demands of real-world environments and operate under assumptions that are frequently violated in real-world industrial deployments.

Self-supervised learning has emerged as a powerful technique in computer vision, demonstrating remarkable performance in various downstream tasks by leveraging unlabeled data. Among these methods, contrastive learning has proven particularly promising by effectively learning image representations. However, its high reliance on large computational resources poses significant practical challenges. To address this issue, there is a pressing need to improve efficiency without compromising generalization performance and robustness. In this paper, we propose Mixup Your Own Latent (MYOL), a regularization method to improve the generalization performance and robustness of Bootstrap Your Own Latent (BYOL), particularly for small images under limited computational resources. MYOL achieves this using the Mixup of the representations of two input images as the target representation of the Mixup of those images. Through experiments conducted in a single GPU environment, we demonstrate that MYOL outperforms BYOL and other regularization methods across various downstream tasks on small-image datasets. The high resilience of MYOL to small batch sizes and its robustness to adversarial attacks further highlight its effectiveness in mitigating the limitations of BYOL. The source code is available at https://github.com/cneyang/MYOL-MixupYourOwnLatent.

The existing multi-class classification algorithms based on support vector machine (SVM) generally decompose the original problem into smaller subproblems. However, the decomposition approach raises the problems of unreliable and unbalanced training of individual classifiers. The aim of this work is to alleviate these problems. In this paper, a novel multi-class classification algorithm based on one-class SVM is presented. The distinguishing point of our proposed algorithm is that the algorithm solves a one-class classification problem rather than decomposing the problem into several smaller subproblems. The proposed algorithm solves a multi-class classification problem by expanding the training input vector with the class label and constructing a one-class SVM with the expanded input vectors. In the test phase, test input vectors are assessed by fit to the decision boundary under the assumption of belonging to a class. We conducted experiments on several real-world benchmark datasets. Experimental results showed that the proposed algorithm outperforms other SVM-based multi-class classification algorithms.