Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey

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A survey of methods for explaining black box models

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Causability and explainability of artificial intelligence in medicine

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Positive and unlabeled learning algorithms and applications: A survey

Kristen Jaskie and Andreas Spanias. 2019 · 2019

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Semantic image synthesis with spatially-adaptive normalization

Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. 2019 · 2019

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Learning from positive and unlabeled data: A survey

Jessa Bekker and Jesse Davis. 2020 · 2020

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Language models are few-shot learners

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020

Anovl: Adapting vision-language models for unified zero-shot anomaly localization

Original

Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao, and Xingyu Li. 2023 · 2023

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Semantic anomaly detection with large language models

Amine Elhafsi, Rohan Sinha, Christopher Agia, Edward Schmerling, Issa AD Nesnas, and Marco Pavone. 2023 · 2023

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Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models

Original

Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, and Roy Ka-Wei Lee. 2023 · 2023

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Winclip: Zero-/few-shot anomaly classification and segmentation

Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer. 2023 · 2023

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Zero-shot in-distribution detection in multi-object settings using vision-language foundation models

Original