همایش بین المللی هوش مصنوعی

صفحه اصلی / دومین همایش بین المللی هوش مصنوعی

DRL-Guided Neural Batch Sampling for Semi-Supervised Pixel-Level Anomaly Detection

نویسندگان :

Amirhossein Khadivi Noghredeh¹ Abdollah Safari² Fatemeh Ziaeetabar³ Firoozeh Haghighi⁴

1- University of Tehran 2- University of Tehran 3- University of Tehran 4- University of Tehran

کلمات کلیدی :

Anomaly detection،semi-supervised learning،REINFORCE،neural batch sampler،MVTec AD

چکیده :

Anomaly detection in industrial visual inspection is challenging due to the scarcity of defective samples. Most existing methods rely on unsupervised reconstruction using only normal data, often resulting in overfitting and poor detection of subtle defects. We propose a semi-supervised deep reinforcement learning framework that integrates a neural batch sampler, an autoencoder, and a predictor. The RL-based sampler adaptively selects informative patches by balancing exploration and exploitation through a composite reward. The autoencoder generates loss profiles highlighting abnormal regions, while the predictor performs segmentation in the loss-profile space. This interaction enables the system to effectively learn both normal and defective patterns with limited labeled data. Experiments on the MVTec AD dataset demonstrate that our method achieves higher accuracy and better localization of subtle anomalies than recent state-of-the-art approaches while maintaining low complexity, yielding an average improvement of 0.15 in F1_max and 0.06 in AUC, with a maximum gain of 0.37 in F1_max in the best case.