Machine Learning & Applications (MaLA)

Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”

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Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"

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Official implementation of CVPR'24 paper 'Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts'.

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Official implementation of CVPR'24 paper 'Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection'.

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The official implementation of CVPR 24' Paper "Learning Transferable Negative Prompts for Out-of-Distribution Detection"

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Implementation of CVPR'23 paper "WinCLIP: Zero-/few-shot anomaly classification and segmentation". It successfully reproduces the same zero-/few-shot AD performance as that in the original paper.

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Code Implementation of "Simple Image-level Classification Improves Open-vocabulary Object Detection" (AAAI'24)

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Official implementation of KDD'23 paper "Deep Weakly-supervised Anomaly Detection"

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Official PyTorch implementation of KDD2025 paper "AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection"

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Official PyTorch implementation of ICCV'25 paper "Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection".

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