arXiv:2504.02775v2 Announce Type: replace-cross Abstract: We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a model is robust against pixel noise, then its performance deteriorates on tail class samples, and vice versa. To mitigate the issue, we handle the tail class and noise samples independently. To this end, we propose TailSampler, a novel class size
The increasing complexity and scale of AI applications, especially in unsupervised settings, necessitate more robust anomaly detection methods to handle diverse and challenging real-world data.
Improved unsupervised anomaly detection, particularly in 'few-shot' scenarios, is crucial for industrial automation, quality control, and predictive maintenance where large labeled datasets are unavailable.
This research introduces a novel approach to overcome the trade-off between noise robustness and tail class performance, potentially leading to more reliable and adaptable AI systems in critical applications.
- · Manufacturing sector
- · AI/ML developers working on quality control
- · Robotics and automation companies
- · Companies reliant on manual inspection
- · Legacy anomaly detection software providers
More accurate and efficient detection of defects and anomalies in production lines without extensive human oversight.
Reduced operational costs and improved product quality across various industries, accelerating automation adoption.
Enhanced resilience and safety of autonomous systems by detecting subtle, complex failures that are currently missed.
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Read at arXiv cs.LG