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In this work, we present DEcoupLEd Distillation To Erase (DELETE), a general and strong unlearning method for any class-centric tasks.
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The eu general data protection regulation (gdpr)
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A novel online incremental and decremental learning algorithm based on variable support vector machine
Yuantao Chen, Jie Xiong, Weihong Xu, and Jingwen Zuo · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Ximing Qiao, Yukun Yang, and Hai Li · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
V Sanh · 2019
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Tao Wang, Li Yuan, Xiaopeng Zhang, and Jiashi Feng · 2019
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Machine unlearning
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Jonathan Brophy and Daniel Lowd · 2021
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Excavating AI: the politics of images in machine learning training sets
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Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2023
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Chongyu Fan, Jiancheng Liu, Yihua Zhang, Eric Wong, Dennis Wei, and Sijia Liu · 2023
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Generating anomalies for video anomaly detection with prompt-based feature mapping
Zuhao Liu, Xiao-Ming Wu, Dian Zheng, Kun-Yu Lin, and Wei-Shi Zheng · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
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Glaze: Protecting artists from style mimicry by text-to-image models
Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y Zhao · 2023
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Knowledge distillation: A survey
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Amnesiac machine learning
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Mlcapsule: Guarded offline deployment of machine learning as a service
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Neural attention distillation: Erasing backdoor triggers from deep neural networks
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, and Xingjun Ma · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
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Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 2023
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Estimator meets equilibrium perspective: A rectified straight through estimator for binary neural networks training
Xiao-Ming Wu, Dian Zheng, Zuhao Liu, and Wei-Shi Zheng · 2023
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Machine unlearning: A survey
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Grids: Grouped multiple-degradation restoration with image degradation similarity
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Learning to unlearn: Instance-wise unlearning for pre-trained classifiers
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Fast machine unlearning without retraining through selective synaptic dampening
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Towards unbounded machine unlearning
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Machine unlearning: Taxonomy, metrics, applications, challenges, and prospects
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Bridge past and future: Overcoming information asymmetry in incremental object detection
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Fair machine unlearning: Data removal while mitigating disparities
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Label-agnostic forgetting: A supervision-free unlearning in deep models
Shaofei Shen, Chenhao Zhang, Yawen Zhao, Alina Bialkowski, Weitong Tony Chen, and Miao Xu · 2024
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Rethinking few-shot class-incremental learning: Learning from yourself
Yu-Ming Tang, Yi-Xing Peng, Jingke Meng, and Wei-Shi Zheng · 2024
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An economic framework for 6-dof grasp detection
Xiao-Ming Wu, Jia-Feng Cai, Jian-Jian Jiang, Dian Zheng, Yi-Lin Wei, and Wei-Shi Zheng · 2024
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Dexterous grasp transformer
Guo-Hao Xu, Yi-Lin Wei, Dian Zheng, Xiao-Ming Wu, and Wei-Shi Zheng · 2024
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Continual forgetting for pre-trained vision models
Hongbo Zhao, Bolin Ni, Junsong Fan, Yuxi Wang, Yuntao Chen, Gaofeng Meng, and Zhaoxiang Zhang · 2024
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Selective hourglass mapping for universal image restoration based on diffusion model
Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, and Wei-Shi Zheng · 2024
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