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Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL), by selecting and updating relevant prompts in the vision-transformer models.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions
Roger Ratcliff · 1990
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Adam: A Method for Stochastic Optimization
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AutoAugment: Learning Augmentation Strategies From Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, and Quoc V. Le · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
Danruo Deng, Guangyong Chen, Jianye Hao, Qiong Wang, and Pheng-Ann Heng · 2021
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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ImageNet-21K Pretraining for the Masses
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Gradient Projection Memory for Continual Learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
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Training Networks in Null Space of Feature Covariance for Continual Learning
Shipeng Wang, Xiaorong Li, Jian Sun, and Zongben Xu · 2021
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Visual Prompt Tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge J. Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers
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CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning
James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogerio Feris, and Zsolt Kira · 2023
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When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Yu-Ming Tang, Yi-Xing Peng, and Wei-Shi Zheng · 2023
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Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, and Jun Zhu · 2023
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AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang, Jianzhuang Liu, Shaohui Lin, Songcen Xu, Jinhu Lv, and Baochang Zhang · 2023
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Yajing Kong, Liu Liu, Zhen Wang, and Dacheng Tao · 2022
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S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning
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DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning
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Learning to Prompt for Continual Learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
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À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
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Reproducible Scaling Laws for Contrastive Language-Image Learning
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A Unified Continual Learning Framework with General Parameter-Efficient Tuning
Qiankun Gao, Chen Zhao, Yifan Sun, Teng Xi, Gang Zhang, Bernard Ghanem, and Jian Zhang · 2023
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Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference
Yabin Wang, Zhiheng Ma, Zhiwu Huang, Yaowei Wang, Zhou Su, and Xiaopeng Hong · 2023
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SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
Gengwei Zhang, Liyuan Wang, Guoliang Kang, Ling Chen, and Yunchao Wei · 2023
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Rethinking Gradient Projection Continual Learning: Stability/Plasticity Feature Space Decoupling
Zhen Zhao, Zhizhong Zhang, Xin Tan, Jun Liu, Yanyun Qu, Yuan Xie, and Lizhuang Ma · 2023
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Consistent Prompting for Rehearsal-Free Continual Learning
Zhanxin Gao, Jun Cen, and Xiaobin Chang · 2024
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OVOR: OnePrompt with virtual outlier regularization for rehearsal-free class-incremental learning
Wei-Cheng Huang, Chun-Fu Chen, and Hsiang Hsu · 2024
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Evolving Parameterized Prompt Memory for Continual Learning
Muhammad Rifki Kurniawan, Xiang Song, Zhiheng Ma, Yuhang He, Yihong Gong, Yang Qi, and Xing Wei · 2024
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Steering Prototypes with Prompt-Tuning for Rehearsal-Free Continual Learning
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InfLoRA: Interference-free low-rank adaptation for continual learning
Yan-Shuo Liang and Wu-Jun Li · 2024
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Prompt Gradient Projection for Continual Learning
Jingyang Qiao, Zhizhong Zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yong Peng, and Yuan Xie · 2024
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Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning
Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, and De-Chuan Zhan · 2024
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