Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions

• URL: http://arxiv.org/abs/2508.02477v1
• Date: Mon, 04 Aug 2025 14:44:40 GMT
• Title: Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions
• Authors: Jaehyuk Heo, Pilsung Kang,
• Abstract summary: We propose Hierarchical Coreset (HierCore) to meet all defined requirements for anomaly detection.<n>HierCore operates effectively even without class labels, leveraging a hierarchical memory bank to estimate class-wise decision criteria.<n>Results show that HierCore consistently meets all requirements and maintains strong, stable performance across all settings.
• Score: 3.647905567437244
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in image anomaly detection have extended unsupervised learning-based models from single-class settings to multi-class frameworks, aiming to improve efficiency in training time and model storage. When a single model is trained to handle multiple classes, it often underperforms compared to class-specific models in terms of per-class detection accuracy. Accordingly, previous studies have primarily focused on narrowing this performance gap. However, the way class information is used, or not used, remains a relatively understudied factor that could influence how detection thresholds are defined in multi-class image anomaly detection. These thresholds, whether class-specific or class-agnostic, significantly affect detection outcomes. In this study, we identify and formalize the requirements that a multi-class image anomaly detection model must satisfy under different conditions, depending on whether class labels are available during training and evaluation. We then re-examine existing methods under these criteria. To meet these challenges, we propose Hierarchical Coreset (HierCore), a novel framework designed to satisfy all defined requirements. HierCore operates effectively even without class labels, leveraging a hierarchical memory bank to estimate class-wise decision criteria for anomaly detection. We empirically validate the applicability and robustness of existing methods and HierCore under four distinct scenarios, determined by the presence or absence of class labels in the training and evaluation phases. The experimental results demonstrate that HierCore consistently meets all requirements and maintains strong, stable performance across all settings, highlighting its practical potential for real-world multi-class anomaly detection tasks.

Related papers

• Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection [18.797864512898787]
  In anomaly detection, early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource management.<n>We investigate this performance observed in reconstruction-based methods, identifying the key issue: inter-class confusion.<n>This confusion emerges when a model trained in multi-class scenarios incorrectly reconstructs samples from one class as those of another, thereby exacerbating reconstruction errors.<n>By explicitly leveraging raw object category information (eg carpet or wood), we introduce local CL to refine multiscale dense features, and global CL to obtain more compact feature representations of normal patterns, thereby effectively adapting the models to multi-class
  arXiv  Detail & Related papers  (2024-12-06T04:31:09Z)
• Fine-Grained ImageNet Classification in the Wild [0.0]
  Robustness tests can uncover several vulnerabilities and biases which go unnoticed during the typical model evaluation stage. In our work, we perform fine-grained classification on closely related categories, which are identified with the help of hierarchical knowledge.
  arXiv  Detail & Related papers  (2023-03-04T12:25:07Z)
• Anomaly Detection using Ensemble Classification and Evidence Theory [62.997667081978825]
  We present a novel approach for novel detection using ensemble classification and evidence theory. A pool selection strategy is presented to build a solid ensemble classifier. We use uncertainty for the anomaly detection approach.
  arXiv  Detail & Related papers  (2022-12-23T00:50:41Z)
• "Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection [4.371371475735559]
  Anomaly detection is at the forefront of finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability.
  arXiv  Detail & Related papers  (2022-04-19T01:55:58Z)
• Self-supervised Pretraining with Classification Labels for Temporal Activity Detection [54.366236719520565]
  Temporal Activity Detection aims to predict activity classes per frame. Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. This work proposes a novel self-supervised pretraining method for detection leveraging classification labels.
  arXiv  Detail & Related papers  (2021-11-26T18:59:28Z)
• Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection [55.888835686183995]
  We propose a neural network-based meta-learning method for supervised anomaly detection. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods.
  arXiv  Detail & Related papers  (2021-03-01T01:43:04Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax [88.11979569564427]
  We provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution. We propose a novel balanced group softmax (BAGS) module for balancing the classifiers within the detection frameworks through group-wise training. Extensive experiments on the very recent long-tail large vocabulary object recognition benchmark LVIS show that our proposed BAGS significantly improves the performance of detectors.
  arXiv  Detail & Related papers  (2020-06-18T10:24:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.