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arXiv Machine Learning

Efficient Patch-Based Anomaly Detection Fused with Diffusion Driven Generative Modeling for Semiconductor Wafer Bin Map Open Set Anomaly Detection

The paper introduces a hybrid one‑class anomaly detector for semiconductor wafer bin maps that combines a patch‑based student‑teacher model (EfficientAD) with a denoising diffusion probabilistic model (DDPM). Trained on only 700 normal wafers, the fused system achieves an AUROC of 0.9985 on 18,658 held‑out wafers, reducing misclassifications from 852 (DDPM) and 1,412 (EfficientAD) to 618, with statistically significant improvements. Analysis reveals that the two modules make weakly overlapping errors, and that fixed‑weight fusion captures only 40‑70 % of the potential correction, highlighting the need for sample‑adaptive fusion and imbalance‑aware evaluation.

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arXiv Machine Learning
Oct 22

Online Open-World Wafer Defect Discovery using Hyperdimensional Computing

The paper introduces WaferHDC, an online open‑world wafer defect discovery framework that uses hyperdimensional computing to detect and group new defect signatures without retraining. It models normal spatial distributions and dynamically creates persistent defect memory, scaling with new defect modes. On the WM‑811K dataset, WaferHDC outperforms the strongest online baseline by 13.3 p.p. in adjusted Rand index and 10.5 p.p. in normalized mutual information.

By Quanling Zhao, Ava Emami, Tsuyoshi Id\'e, Rajiv Joshi, Steven Holmes, Michael Passow, Tajana Rosing
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arXiv Machine Learning
Oct 12

NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection

NV-Tesseract-AD is a curriculum learning‑enhanced conditional diffusion framework designed for multivariate time‑series anomaly detection. It structures reconstruction difficulty along masking severity and diffusion noise level, using a Transformer‑based diffusion backbone and a three‑stage dual curriculum that keeps anomalous samples throughout training. The method achieves significant performance gains, including a 3.7‑fold F1 improvement and 56% MSE reduction over a non‑curriculum baseline, and demonstrates strong zero‑shot results on several benchmark families.

By Aditi Gautam, Manikandan Ravikiran, Saira Qureshi, Sena Fatma Ekiz, Charan Jagwani, Mahmoud Elhadidy, Sanjeev Roka
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Hugging Face Trending Papers
Aug 19

CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation

The paper introduces Contrastive Dual Gaussian Processes (CDGP), a weakly supervised approach for anomaly segmentation in industrial visual inspection. CDGP models normal and anomaly predictive distributions over dense tokens, using a posterior-dominance statistic that normalizes predictive-mean differences by joint uncertainty to provide spatial evidence and image-level confidence. The method achieves top performance on MVTec AD~2, KSDD2, and VisA datasets without requiring pixel-level annotations or test-time fitting.

computer-vision
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arXiv Computer Vision
Sep 7

Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion

The paper introduces a training‑free anomaly detector that simultaneously handles structural and logical defects by calibrating heterogeneous anomaly cues with statistics from normal images. This calibration aligns frozen representations, allowing their fusion without extra training or part‑level supervision. The resulting method achieves state‑of‑the‑art AUROC scores on MVTec‑LOCO and remains competitive on MVTec‑AD.

By Changyi Li, Miao Yu, Kai Dong, Yu Xiao
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arXiv Computer Vision
Sep 18

Generative Verification: Rethinking the Uncertainty Signal for Active Learning of Object Detection

Generative Verification introduces an active learning strategy for object detection that uses an independent generative model to re‑derive a detection’s label from the pixels inside its predicted box. The disagreement between the detector’s label and the verifier’s label serves as the acquisition signal, automatically combining localization and classification errors into a single scalar and eliminating the need for hand‑weighted terms. Experiments on PASCAL VOC and MS‑COCO show that this signal outperforms traditional uncertainty and ensemble criteria, improving mAP50 by about one point per round, especially in early rounds where confident detector errors are most common.

By Licheng Zhang, Zheng Gong
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Hugging Face Trending Papers
Aug 19

SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detection

SPARC is a few‑shot calibration technique for vision‑based industrial anomaly detectors that corrects deployment‑time nuisances by projecting patch features onto a per‑cell subspace, requiring only up to eight verified‑normal images and no gradient updates. It operates between the encoder and detector, using a closed‑form, spatially indexed estimate based on the encoder’s native patch grid. Across seven detectors on shift‑prone benchmarks, SPARC boosts pooled Image AUROC by 13.8 pp and AU‑PRO₀.₃ by 3.5 pp, while showing modest changes on benchmarks without engineered shift.

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