Learning Prompts with Only Normal Samples for Efficient Few-Shot Anomaly Detection

Core Concepts

A one-class prompt learning method, termed PromptAD, is proposed to efficiently learn prompts with only normal samples for few-shot anomaly detection.

Abstract

The paper proposes a one-class prompt learning method, PromptAD, for efficient few-shot anomaly detection. The key highlights are: Semantic Concatenation (SC): To address the lack of negative samples in one-class anomaly detection, SC transposes the semantics of normal prompts by concatenating them with anomaly suffixes, constructing a large number of negative prompts to guide prompt learning. Explicit Anomaly Margin (EAM): Since anomaly samples are unavailable during training, EAM introduces a hyper-parameter to explicitly control the margin between normal prompt features and anomaly prompt features, mitigating the training challenge. PromptAD achieves state-of-the-art performance on both image-level and pixel-level anomaly detection in 11 out of 12 few-shot settings on the MVTec and VisA benchmarks, significantly outperforming existing prompt-guided methods. Extensive experiments and ablation studies verify the effectiveness of the proposed SC and EAM modules, as well as the superiority of PromptAD over conventional prompt learning baselines in the one-class anomaly detection scenario.

Stats

Only normal samples are available during training, but the model is expected to identify anomalous samples in the testing phase. The MVTec dataset contains 15 objects with 7002-9002 pixels per image, and the VisA dataset contains 12 objects with roughly 1.5K × 1K pixels per image. The Area Under the Receiver Operation Characteristic (AUROC) is used as the evaluation metric for both image-level and pixel-level anomaly detection.

Quotes

"Semantic concatenation (SC) is proposed, which can transpose the semantics of normal prompts by concatenating anomaly suffixes, so as to construct enough negative prompts for normal samples." "Explicit anomaly margin (EAM) is proposed, which can explicitly control the distance between normal prompt features and anomaly prompt features through a hyper-parameter."

Key Insights Distilled From

PromptAD

by Xiaofan Li,Z... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05231.pdf

Deeper Inquiries

How can the proposed PromptAD framework be extended to other one-class classification tasks beyond anomaly detection?

The PromptAD framework can be extended to other one-class classification tasks by adapting the semantic concatenation and explicit anomaly margin concepts to suit the specific characteristics of the new tasks. For example, in tasks such as fraud detection or rare event prediction, where only normal samples are available during training, the semantic concatenation approach can be used to generate negative prompts for contrastive learning. By concatenating normal prompts with specific fraud indicators or rare event characteristics, a large number of negative samples can be created to guide the learning process. Additionally, the explicit anomaly margin concept can be applied to control the distance between normal and anomaly prompt features, ensuring a clear margin for classification.

What are the potential limitations of the semantic concatenation approach, and how can it be further improved to handle more diverse anomaly types?

One potential limitation of the semantic concatenation approach is the reliance on manually curated anomaly suffixes, which may not cover the full spectrum of diverse anomaly types in real-world scenarios. To address this limitation and improve the approach, a few strategies can be implemented: Automated Anomaly Suffix Generation: Develop algorithms to automatically generate anomaly suffixes based on the characteristics of the dataset. This can help in covering a wider range of anomaly types without manual intervention. Dynamic Semantic Concatenation: Implement a dynamic approach where the system learns to generate anomaly suffixes based on the anomalies present in the dataset. This adaptive strategy can enhance the diversity and coverage of anomaly types.

Can the explicit anomaly margin concept be generalized to other one-class learning settings, and what are the theoretical insights behind its effectiveness?

Yes, the explicit anomaly margin concept can be generalized to other one-class learning settings beyond anomaly detection. The theoretical insight behind its effectiveness lies in the principle of creating a clear separation between normal and anomaly features in the feature space. By explicitly controlling the margin between normal prompt features and anomaly prompt features, the model can learn to distinguish anomalies more effectively. This concept can be applied to tasks such as rare event detection, fraud detection, or any other task where only normal samples are available during training. The margin ensures that the model has a clear boundary for classification, leading to improved performance in one-class learning scenarios.