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Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al. 2009 · 2009
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann. 2017 · 2017
Earlier work this paper cites.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2019 · 2021
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. 2021 · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. 2021 · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021 · 2021
Earlier work this paper cites.
Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A Rossi, and Raman Arora. 2021 · 2021
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Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al. 2021 · 2021
Earlier work this paper cites.
Diffusionclip: Text-guided diffusion models for robust image manipulation
Gwanghyun Kim, Taesung Kwon, and Jong-Chul Ye. 2021 · 2022
Earlier work this paper cites.
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022 · 2022
Earlier work this paper cites.
Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone. 2022 · 2022
Earlier work this paper cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Cited alongside, same era.
Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. 2022 · 2022
Cited alongside, same era.
Real-time vehicle detection based on improved yolo v5
Yu Zhang, Zhongyin Guo, Jianqing Wu, Yuan Tian, Haotian Tang, and Xinming Guo. 2022 · 2022
Cited alongside, same era.
Unlearn what you want to forget: Efficient unlearning for llms
Jiaao Chen and Diyi Yang. 2023 · 2023
Cited alongside, same era.
Boundary unlearning: Rapid forgetting of deep networks via shifting the decision boundary
Min Chen, Weizhuo Gao, Gaoyang Liu, Kai Peng, and Chen Wang. 2023 · 2023
Cited alongside, same era.
Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Later among the works it cites.
Fedrecovery: Differentially private machine unlearning for federated learning frameworks
Lefeng Zhang, Tianqing Zhu, Haibin Zhang, Ping Xiong, and Wanlei Zhou. 2023 · 2023
Later among the works it cites.
Erasing concepts from text-to-image diffusion models with few-shot unlearning
Masane Fuchi and Tomohiro Takagi. 2024 · 2024
Closest in time.
Unified concept editing in diffusion models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzyńska, and David Bau. 2024 · 2024
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Soul: Unlocking the power of second-order optimization for llm unlearning
Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, and Sijia Liu. 2024 · 2024
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Jiali Cheng and Hadi Amiri. 2023 · 2023
Cited alongside, same era.
Zero-shot machine unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli. 2023 · 2023
Cited alongside, same era.
Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich. 2023 · 2023
Cited alongside, same era.
Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau. 2023 · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
Cited alongside, same era.
Kga: A general machine unlearning framework based on knowledge gap alignment
Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin. 2023 · 2023
Cited alongside, same era.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024a
Cited in the paper.
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Zero-shot class unlearning in clip with synthetic samples
Alexey Kravets and Vinay Namboodiri. 2024 · 2024
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou. 2024 · 2024
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TOFU: A task of fictitious unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary Chase Lipton, and J Zico Kolter. 2024 · 2024
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Teaching large language models to "forget" unwanted content
Anthony McConnon. 2024 · 2024
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Announcing the first machine unlearning challenge
Fabian Pedregosa and Eleni Triantafillou. 2023 · 2024
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Forget-me-not: Learning to forget in text-to-image diffusion models
Gong Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi. 2024 · 2024
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