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Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt (2401.01010v1)

Published 2 Jan 2024 in cs.CV and cs.LG

Abstract: Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. However, continual learning methods primarily rely on supervised annotations, while the application in UAD is limited due to the absence of supervision. Current UAD methods train separate models for different classes sequentially, leading to catastrophic forgetting and a heavy computational burden. To address this issue, we introduce a novel Unsupervised Continual Anomaly Detection framework called UCAD, which equips the UAD with continual learning capability through contrastively-learned prompts. In the proposed UCAD, we design a Continual Prompting Module (CPM) by utilizing a concise key-prompt-knowledge memory bank to guide task-invariant anomaly' model predictions using task-specificnormal' knowledge. Moreover, Structure-based Contrastive Learning (SCL) is designed with the Segment Anything Model (SAM) to improve prompt learning and anomaly segmentation results. Specifically, by treating SAM's masks as structure, we draw features within the same mask closer and push others apart for general feature representations. We conduct comprehensive experiments and set the benchmark on unsupervised continual anomaly detection and segmentation, demonstrating that our method is significantly better than anomaly detection methods, even with rehearsal training. The code will be available at https://github.com/shirowalker/UCAD.

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An Overview of Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt

The paper at hand introduces a novel approach to Unsupervised Continual Anomaly Detection (UCAD) with an emphasis on contrastively-learned prompts. The research addresses significant challenges in industrial manufacturing, where obtaining labeled defect data is often impractical. The authors point out a critical gap in existing models: the application of continual learning (CL) methods predominantly depends on supervised annotations, making them less suitable for unsupervised anomaly detection due to the lack of supervision.

To overcome these issues, the paper proposes the UCAD framework, which uses a Continual Prompting Module (CPM) to instill continual learning capabilities in unsupervised anomaly detection (UAD) models. Additionally, the researchers have introduced a Structure-based Contrastive Learning (SCL) module, leveraging the Segment Anything Model (SAM) to optimize prompt learning and anomaly segmentation.

Key Contributions

The research proposes several novel methodologies, which are key to its proposed solution:

  • Continual Prompting Module (CPM): This module employs a key-prompt-knowledge architecture, comprised of a memory bank that helps guide task-invariant anomaly predictions with task-specific knowledge. The method allows the model to automatically select the relevant task queries and adapt to incoming image data without prior knowledge of task identities during the inference phase.
  • Structure-based Contrastive Learning (SCL): This component enhances feature representation by treating SAM masks as structures to guide learning. It contrasts features within the same mask with those from different masks, promoting more discriminative and general feature representations.

The UCAD framework significantly lowers computational overhead by requiring only the current dataset during training and allowing model application to prior tasks without a complete retraining.

Results and Implications

The research demonstrates impressive results, with the proposed UCAD method surpassing existing anomaly detection methods when evaluated on benchmarks for unsupervised continual anomaly detection and segmentation. The authors report a 15.6% improvement in detection and a 26.6% improvement in segmentation over other methods, even those leveraging rehearsal training.

These results indicate UCAD's potential to transform how unsupervised anomaly detection is approached within industrial environments, offering a continual learning model that adapts to new tasks while retaining knowledge of previous ones. This advancement holds significance for real-world applications, particularly in industries where labeling anomalies is costly and defect patterns can vary over time.

Conclusion and Future Prospects

The paper presents a novel and effective approach to anomaly detection in a continual learning framework, addressing some of the practical challenges faced in unsupervised learning scenarios. By removing the dependency on supervised labels and introducing a robust CL system, the proposed model offers significant advancements for industrial manufacturing settings.

Looking forward, there are multiple avenues for further research and development. The combination of contrastively-learned prompts with other model architectures or domains could yield additional improvements, potentially expanding this framework to other real-world applications where continual learning and unsupervised methodologies intersect. Additionally, exploring extensions of this approach to other domains beyond industrial manufacturing may further validate the robustness and versatility of this framework.

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Authors (9)
  1. Jiaqi Liu (102 papers)
  2. Kai Wu (134 papers)
  3. Qiang Nie (25 papers)
  4. Ying Chen (333 papers)
  5. Bin-Bin Gao (35 papers)
  6. Yong Liu (721 papers)
  7. Jinbao Wang (30 papers)
  8. Chengjie Wang (178 papers)
  9. Feng Zheng (117 papers)
Citations (8)
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