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Mixture of Experts (MoE) architectures have recently advanced the scalability and adaptability of large language models (LLMs) for continual multimodal learning.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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ReferItGame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
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Variational autoencoder based anomaly detection using reconstruction probability, 2015
Jinwon An and Sungzoon Cho · 2015
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Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin · 2016
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Neuroscience-inspired artificial intelligence
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, QuocV. Le, GeoffreyE. Hinton, and Jeff Dean · 2017
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Continual learning and catastrophic forgetting
Zhiyuan Chen and Bing Liu · 2018
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VizWiz Grand Challenge: Answering Visual Questions from Blind People
Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham · 2018
Earlier work this paper cites.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Overcoming catastrophic forgetting with hard attention to the task, 2018
Joan Serrà, Dídac Surís, Marius Miron, and Alexandros Karatzoglou · 2018
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Towards VQA models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
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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
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Ratt: Recurrent attention to transient tasks for continual image captioning, 2020
Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov, and Joost van de Weijer · 2020
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Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
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Gshard: Scaling giant models with conditional computation and automatic sharding, 2020
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
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Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
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Hierarchical visual-textual knowledge distillation for life-long correlation learning
Yuxin Peng, Jinwei Qi, Zhaoda Ye, and Yunkan Zhuo · 2021
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Variational autoencoder
Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, and Sérgio Lima Netto · 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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Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Multimodal continual learning using online dictionary updating
Fuchun Sun, Huaping Liu, Chao Yang, and Bin Fang · 2021
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Counter-interference adapter for multilingual machine translation
Yaoming Zhu, Jiangtao Feng, Chengqi Zhao, Mingxuan Wang, and Lei Li · 2021
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Decouple before interact: Multi-modal prompt learning for continual visual question answering
Zi Qian, Xin Wang, Xuguang Duan, Pengda Qin, Yuhong Li, and Wenwu Zhu · 2023
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Scaling vision-language models with sparse mixture of experts
Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, and Yuxiong He · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning, 2023
Yabin Wang, Zhiwu Huang, and Xiaopeng Hong · 2023
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Multimodal continual graph learning with neural architecture search
Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, and Wenwu Zhu · 2022
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Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
Yifan Chen, Devamanyu Hazarika, Mahdi Namazifar, Yang Liu, Di Jin, and Dilek Hakkani-Tur · 2022
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Krona: Parameter efficient tuning with kronecker adapter
Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J Clark, and Mehdi Rezagholizadeh · 2022
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2022
William Fedus, Barret Zoph, and Noam Shazeer · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
Cited alongside, same era.
P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2022
Cited alongside, same era.
Multimodal contrastive learning with limoe: the language-image mixture of experts
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, and Neil Houlsby · 2022
Cited alongside, same era.
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Multimodal Large Language Models: A Survey
Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S. Yu · 2023
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Knowledge decomposition and replay: A novel cross-modal image-text retrieval continual learning method
Rui Yang, Shuang Wang, Huan Zhang, Siyuan Xu, YanHe Guo, Xiutiao Ye, Biao Hou, and Licheng Jiao · 2023
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Preventing zero-shot transfer degradation in continual learning of vision-language models
Zangwei Zheng, Mingyu Ma, Kai Wang, Ziheng Qin, Xiangyu Yue, and Yang You · 2023
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Parameter-efficient fine-tuning of large pretrained models for instance segmentation tasks
Nermeen Abou Baker, David Rohrschneider, and Uwe Handmann · 2024
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The revolution of multimodal large language models: A survey
Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, and Rita Cucchiara · 2024
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Coin: A benchmark of continual instruction tuning for multimodel large language models
Cheng Chen, Junchen Zhu, Xu Luo, Hengtao Shen, Jingkuan Song, and Lianli Gao · 2024
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Parameter-efficient fine-tuning for large models: A comprehensive survey, 2024
Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, and Sai Qian Zhang · 2024
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Clap4clip: Continual learning with probabilistic finetuning for vision-language models, 2024
Saurav Jha, Dong Gong, and Lina Yao · 2024
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Mixtral of experts, 2024
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
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When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications
Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, and Yefeng Zheng · 2024
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Tongxu Luo, Jiahe Lei, Fangyu Lei, Weihao Liu, Shizhu He, Jun Zhao, and Kang Liu · 2024
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Continual learning of large language models: A comprehensive survey
Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, and Hao Wang · 2024
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Lemoe: Advanced mixture of experts adaptor for lifelong model editing of large language models
Renzhi Wang and Piji Li · 2024
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Continual learning for large language models: A survey, 2024
Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari · 2024
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Moral: Moe augmented lora for llms’ lifelong learning, 2024
Shu Yang, Muhammad Asif Ali, Cheng-Long Wang, Lijie Hu, and Di Wang · 2024
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Boosting continual learning of vision-language models via mixture-of-experts adapters
Jiazuo Yu, Yunzhi Zhuge, Lu Zhang, Ping Hu, Dong Wang, Huchuan Lu, and You He · 2024
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Beyond anti-forgetting: Multimodal continual instruction tuning with positive forward transfer, 2024
Junhao Zheng, Qianli Ma, Zhen Liu, Binquan Wu, and Huawen Feng · 2024
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Moextend: Tuning new experts for modality and task extension
Shanshan Zhong, Shanghua Gao, Zhongzhan Huang, Wushao Wen, Marinka Zitnik, and Pan Zhou · 2024
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