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For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays.
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer, “Membership inference attacks from first principles,” in 2022 IEEE symposium on security and privacy (SP) . IEEE, 2022, pp. 1897–1914
1914
Earlier work this paper cites.
S. Kullback, Information theory and statistics . Courier Corporation, 1997
1997
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A perspective view and survey of meta-learning,” Artificial intelligence review , vol. 18, pp. 77–95, 2002
2002
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” in 2004 conference on computer vision and pattern recognition workshop . IEEE, 2004, pp. 178–178
2004
Earlier work this paper cites.
F. Fleuret and G. Blanchard, “Pattern recognition from one example by chopping,” Advances in neural information processing systems , vol. 18, 2005
2005
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “One-shot learning of object categories,” IEEE transactions on pattern analysis and machine intelligence , vol. 28, no. 4, pp. 594–611, 2006
2006
Earlier work this paper cites.
T. Hertz, A. B. Hillel, and D. Weinshall, “Learning a kernel function for classification with small training samples,” in Proceedings of the 23rd international conference on Machine learning , 2006, pp. 401–408
2006
Earlier work this paper cites.
M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society Series B: Statistical Methodology , vol. 68, no. 1, pp. 49–67, 2006
2006
Earlier work this paper cites.
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 2001–2010
2010
Earlier work this paper cites.
X. Yu and Y. Aloimonos, “Attribute-based transfer learning for object categorization with zero/one training example,” in Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part V 11 . Springer, 2010, pp. 127–140
2010
Earlier work this paper cites.
A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in 2010 20th international conference on pattern recognition . IEEE, 2010, pp. 2366–2369
2010
Earlier work this paper cites.
R. Salakhutdinov, J. Tenenbaum, and A. Torralba, “One-shot learning with a hierarchical nonparametric bayesian model,” in Proceedings of ICML Workshop on Unsupervised and Transfer Learning . JMLR Workshop and Conference Proceedings, 2012, pp. 195–206
2012
Earlier work this paper cites.
S. E. Yuksel, J. N. Wilson, and P. D. Gader, “Twenty years of mixture of experts,” IEEE transactions on neural networks and learning systems , vol. 23, no. 8, pp. 1177–1193, 2012
2012
Earlier work this paper cites.
C. H. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification for zero-shot visual object categorization,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 3, pp. 453–465, 2013
2013
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
J. Feng and T. Darrell, “Learning the structure of deep convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2749–2757
2015
Earlier work this paper cites.
A. Wong and A. L. Yuille, “One shot learning via compositions of meaningful patches,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1197–1205
2015
Earlier work this paper cites.
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science , vol. 350, no. 6266, pp. 1332–1338, 2015
2015
Earlier work this paper cites.
G. Koch, R. Zemel, R. Salakhutdinov et al. , “Siamese neural networks for one-shot image recognition,” in ICML deep learning workshop , vol. 2, no. 1. Lille, 2015, pp. 1–30
2015
Earlier work this paper cites.
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky, “Sparse convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 806–814
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, pp. 211–252, 2015
2015
Earlier work this paper cites.
Z. Izzo, M. A. Smart, K. Chaudhuri, and J. Zou, “Approximate data deletion from machine learning models,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 2008–2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
H. Edwards and A. Storkey, “Towards a neural statistician,” arXiv preprint arXiv:1606.02185 , 2016
2016
Earlier work this paper cites.
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi, “Learning feed-forward one-shot learners,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
R. Kwitt, S. Hegenbart, and M. Niethammer, “One-shot learning of scene locations via feature trajectory transfer,” in Proceedings of The IEEE conference on computer vision and pattern recognition , 2016, pp. 78–86
2016
Earlier work this paper cites.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Earlier work this paper cites.
H. Shin, J. K. Lee, J. Kim, and J. Kim, “Continual learning with deep generative replay,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Z. Li and D. Hoiem, “Learning without forgetting,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 12, pp. 2935–2947, 2017
2017
Earlier work this paper cites.
R. Aljundi, P. Chakravarty, and T. Tuytelaars, “Expert gate: Lifelong learning with a network of experts,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3366–3375
2017
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in International conference on learning representations , 2017
2017
Earlier work this paper cites.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
M. Dixit, R. Kwitt, M. Niethammer, and N. Vasconcelos, “Aga: Attribute-guided augmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7455–7463
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE symposium on security and privacy (SP) . IEEE, 2017, pp. 3–18
2017
Earlier work this paper cites.
G. D. P. Regulation, “General data protection regulation (gdpr),” Intersoft Consulting, Accessed in October , vol. 24, no. 1, 2018
2018
Earlier work this paper cites.
A. Mallya, D. Davis, and S. Lazebnik, “Piggyback: Adapting a single network to multiple tasks by learning to mask weights,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 67–82
2018
Earlier work this paper cites.
A. Mallya and S. Lazebnik, “Packnet: Adding multiple tasks to a single network by iterative pruning,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 7765–7773
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Isele and A. Cosgun, “Selective experience replay for lifelong learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars, “Memory aware synapses: Learning what (not) to forget,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 139–154
2018
Earlier work this paper cites.
J. Schwarz, W. Czarnecki, J. Luketina, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, and R. Hadsell, “Progress & compress: A scalable framework for continual learning,” in International conference on machine learning . PMLR, 2018, pp. 4528–4537
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales, “Learning to compare: Relation network for few-shot learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1199–1208
2018
Cited alongside, same era.
Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan, “Low-shot learning from imaginary data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7278–7286
2018
Cited alongside, same era.
T. Scott, K. Ridgeway, and M. C. Mozer, “Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Cited alongside, same era.
H. Gao, Z. Shou, A. Zareian, H. Zhang, and S.-F. Chang, “Low-shot learning via covariance-preserving adversarial augmentation networks,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Cited alongside, same era.
H. Yan, X. Li, Z. Guo, H. Li, F. Li, and X. Lin, “Arcane: An efficient architecture for exact machine unlearning,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 , 2022, pp. 4006–4013
2022
Later among the works it cites.
Z. Wang, Z. Zhang, C.-Y. Lee, H. Zhang, R. Sun, X. Ren, G. Su, V. Perot, J. Dy, and T. Pfister, “Learning to prompt for continual learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 139–149
2022
Later among the works it cites.
C. Zhang, K. Tian, B. Fan, G. Meng, Z. Zhang, and C. Pan, “Continual stereo matching of continuous driving scenes with growing architecture,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 901–18 910
2022
Later among the works it cites.
F. Zhu, X.-Y. Zhang, R.-Q. Wang, and C.-L. Liu, “Learning by seeing more classes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 6, pp. 7477–7493, 2022
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D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9446–9454
2018
Cited alongside, same era.
Y. Chen, J. Xiong, W. Xu, and J. Zuo, “A novel online incremental and decremental learning algorithm based on variable support vector machine,” Cluster Computing , vol. 22, pp. 7435–7445, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in International Conference on Machine Learning . PMLR, 2019, pp. 2790–2799
2019
Cited alongside, same era.
D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne, “Experience replay for continual learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu, “Large scale incremental learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 374–382
2019
Cited alongside, same era.
A. Ginart, M. Guan, G. Valiant, and J. Y. Zou, “Making ai forget you: Data deletion in machine learning,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
2022
Later among the works it cites.
A. Douillard, A. Ramé, G. Couairon, and M. Cord, “Dytox: Transformers for continual learning with dynamic token expansion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 9285–9295
2022
Later among the works it cites.
Z. Wang, Z. Zhang, S. Ebrahimi, R. Sun, H. Zhang, C.-Y. Lee, X. Ren, G. Su, V. Perot, J. Dy et al. , “Dualprompt: Complementary prompting for rehearsal-free continual learning,” European Conference on Computer Vision , 2022
2022
Later among the works it cites.
Y. Wang, Z. Huang, and X. Hong, “S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 5682–5695, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Baumhauer, P. Schöttle, and M. Zeppelzauer, “Machine unlearning: Linear filtration for logit-based classifiers,” Machine Learning , vol. 111, no. 9, pp. 3203–3226, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Ye, Y. Fu, J. Song, X. Yang, S. Liu, X. Jin, M. Song, and X. Wang, “Learning with recoverable forgetting,” in ECCV , 2022
2022
Later among the works it cites.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 000–16 009
2022
Later among the works it cites.
B. Ni, H. Peng, M. Chen, S. Zhang, G. Meng, J. Fu, S. Xiang, and H. Ling, “Expanding language-image pretrained models for general video recognition,” in European Conference on Computer Vision . Springer, 2022, pp. 1–18
2022
Later among the works it cites.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII . Springer, 2022, pp. 709–727
2022
Later among the works it cites.
K. Zhou, J. Yang, C. C. Loy, and Z. Liu, “Conditional prompt learning for vision-language models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 816–16 825
2022
Later among the works it cites.
2022
Later among the works it cites.
M. Hou and I. Sato, “A closer look at prototype classifier for few-shot image classification,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 767–25 778, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Hu, Z. Salcic, L. Sun, G. Dobbie, P. S. Yu, and X. Zhang, “Membership inference attacks on machine learning: A survey,” ACM Computing Surveys (CSUR) , vol. 54, no. 11s, pp. 1–37, 2022
2022
Later among the works it cites.
Y. Liu, B. Schiele, A. Vedaldi, and C. Rupprecht, “Continual detection transformer for incremental object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 799–23 808
2023
Later among the works it cites.
2023
Later among the works it cites.
M. U. Khattak, S. T. Wasim, M. Naseer, S. Khan, M.-H. Yang, and F. S. Khan, “Self-regulating prompts: Foundational model adaptation without forgetting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 190–15 200
2023
Later among the works it cites.
J. S. Smith, L. Karlinsky, V. Gutta, P. Cascante-Bonilla, D. Kim, A. Arbelle, R. Panda, R. Feris, and Z. Kira, “Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 11 909–11 919
2023
Later among the works it cites.
F. Zhu, Z. Cheng, X.-Y. Zhang, and C.-L. Liu, “Imitating the oracle: Towards calibrated model for class incremental learning,” Neural Networks , vol. 164, pp. 38–48, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
V. S. Chundawat, A. K. Tarun, M. Mandal, and M. Kankanhalli, “Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 6, 2023, pp. 7210–7217
2023
Later among the works it cites.
——, “Zero-shot machine unlearning,” IEEE Transactions on Information Forensics and Security , vol. 18, pp. 2345–2354, 2023
2023
Later among the works it cites.
N. Sun, N. Wang, Z. Wang, J. Nie, Z. Wei, P. Liu, X. Wang, and H. Qu, “Lazy machine unlearning strategy for random forests,” in International Conference on Web Information Systems and Applications . Springer, 2023, pp. 383–390
2023
Later among the works it cites.
M. Kurmanji, P. Triantafillou, and E. Triantafillou, “Towards unbounded machine unlearning,” Advances in neural information processing systems , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. K. Tarun, V. S. Chundawat, M. Mandal, and M. Kankanhalli, “Fast yet effective machine unlearning,” IEEE Transactions on Neural Networks and Learning Systems , vol. 35, no. 9, pp. 13 046–13 055, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Wang, G. Chen, G. Qian, P. Gao, X.-Y. Wei, Y. Wang, Y. Tian, and W. Gao, “Large-scale multi-modal pre-trained models: A comprehensive survey,” Machine Intelligence Research , vol. 20, no. 4, pp. 447–482, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
R.-Q. Wang, F. Zhu, X.-Y. Zhang, and C.-L. Liu, “Training with scaled logits to alleviate class-level over-fitting in few-shot learning,” Neurocomputing , vol. 522, pp. 142–151, 2023
2023
Later among the works it cites.
J. Lu, P. Gong, J. Ye, J. Zhang, and C. Zhang, “A survey on machine learning from few samples,” Pattern Recognition , vol. 139, p. 109480, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Zhao, B. Ni, J. Fan, Y. Wang, Y. Chen, G. Meng, and Z. Zhang, “Continual forgetting for pre-trained vision models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 28 631–28 642
2024
Later among the works it cites.
2024
Later among the works it cites.
B. Ni, X. Nie, C. Zhang, S. Xu, X. Zhang, G. Meng, and S. Xiang, “Moboo: Memory-boosted vision transformer for class-incremental learning,” IEEE Transactions on Circuits and Systems for Video Technology , 2024
2024
Later among the works it cites.
D. Turner, P. J. Cardoso, and J. M. Rodrigues, “Continual learning for object classification: integrating automl for binary classification tasks within a modular dynamic architecture,” IEEE Access , 2024
2024
Later among the works it cites.
B. Ni, H. Zhao, C. Zhang, K. Hu, G. Meng, Z. Zhang, and S. Xiang, “Enhancing visual continual learning with language-guided supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2024, pp. 24 068–24 077
2024
Later among the works it cites.
J. Liu, P. Ram, Y. Yao, G. Liu, Y. Liu, P. SHARMA, S. Liu et al. , “Model sparsity can simplify machine unlearning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2025
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