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Fine-tuning pre-trained models for downstream tasks has led to a proliferation of open-sourced task-specific models.
A visual vocabulary for flower classification. In IEEE Conference on Computer Vision and Pattern Recognition
M-E Nilsback and Andrew Zisserman. 2006 · 2006
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo. In IEEE Conference on Computer Vision and Pattern Recognition
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. 2010 · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics
Adam Coates, Andrew Ng, and Honglak Lee. 2011 · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al · 2011
Earlier work this paper cites.
The German traffic sign recognition benchmark: a multi-class classification competition. In International Joint Conference on Neural Networks
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. 2011 · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng. 2012 · 2012
Earlier work this paper cites.
Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. 2012 · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization. In IEEE International Conference on Computer Vision Workshops . 554–561
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 2013 · 2013
Earlier work this paper cites.
Describing textures in the wild. In IEEE Conference on Computer Vision and Pattern Recognition
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. 2014 · 2014
Earlier work this paper cites.
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer. 2017 · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. 2017 · 2017
Earlier work this paper cites.
Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu. 2017 · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. 2017 · 2017
Earlier work this paper cites.
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, et al · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018 · 2018
Earlier work this paper cites.
Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019 · 2019
Earlier work this paper cites.
A new backdoor attack in cnns by training set corruption without label poisoning. In IEEE International Conference on Image Processing
Mauro Barni, Kassem Kallas, and Benedetta Tondi. 2019 · 2019
Earlier work this paper cites.
Strip: A defence against trojan attacks on deep neural networks. In Annual Computer Security Applications Conference
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal. 2019 · 2019
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. 2019 · 2019
Earlier work this paper cites.
Abs: Scanning neural networks for back-doors by artificial brain stimulation. In ACM SIGSAC Conference on Computer and Communications Security
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang. 2019 · 2019
Earlier work this paper cites.
Label-consistent backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry. 2019 · 2019
Earlier work this paper cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. In IEEE Symposium on Security and Privacy
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2019 · 2019
Cited alongside, same era.
How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. 2020 · 2020
Cited alongside, same era.
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, et al · 2020
Cited alongside, same era.
Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. 2020 · 2020
Cited alongside, same era.
What is being transferred in transfer learning?
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. 2022 · 2022
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Backdoor learning: A survey
Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia. 2022b · 2022
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Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel. 2022 · 2022
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Dynamic backdoor attacks against machine learning models. In IEEE European Symposium on Security and Privacy
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang. 2022 · 2022
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Corruptencoder: Data poisoning based backdoor attacks to contrastive learning
Jinghuai Zhang, Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong. 2022 · 2022
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Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang. 2020 · 2020
Cited alongside, same era.
Hidden trigger backdoor attacks. In AAAI Conference on Artificial Intelligence
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash. 2020 · 2020
Cited alongside, same era.
Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos. 2020b · 2020
Cited alongside, same era.
Backdoor attacks against transfer learning with pre-trained deep learning models
Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, and Tianle Chen. 2020a · 2020
Cited alongside, same era.
Detection of backdoors in trained classifiers without access to the training set
Zhen Xiang, David J Miller, and George Kesidis. 2020 · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Badnl: Backdoor attacks against nlp models with semantic-preserving improvements. In Annual Computer Security Applications Conference
Xiaoyi Chen, Ahmed Salem, Dingfan Chen, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang. 2021 · 2021
Cited alongside, same era.
Lira: Learnable, imperceptible and robust backdoor attacks. In IEEE/CVF International Conference on Computer Vision
Khoa Doan, Yingjie Lao, Weijie Zhao, and Ping Li. 2021 · 2021
Cited alongside, same era.
Junfeng Guo, Yiming Li, Xun Chen, Hanqing Guo, Lichao Sun, and Cong Liu. 2023 · 2023
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Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin. 2023 · 2023
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On the Difficulty of Defending Contrastive Learning against Backdoor Attacks
Changjiang Li, Ren Pang, Bochuan Cao, Zhaohan Xi, Jinghui Chen, Shouling Ji, and Ting Wang. 2023a · 2023
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An empirical study of catastrophic forgetting in large language models during continual fine-tuning
Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. 2023 · 2023
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Combined scaling for zero-shot transfer learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, et al · 2023
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Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
Anke Tang, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, and Dacheng Tao. 2023 · 2023
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Mm-bd: Post-training detection of backdoor attacks with arbitrary backdoor pattern types using a maximum margin statistic. In IEEE Symposium on Security and Privacy
Hang Wang, Zhen Xiang, David J Miller, and George Kesidis. 2023 · 2023
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Hu Xu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer. 2023 · 2023
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Ties-merging: Resolving interference when merging models. In Advances in Neural Information Processing Systems
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal. 2023 · 2023
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AdaMerging: Adaptive Model Merging for Multi-Task Learning
Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, and Dacheng Tao. 2023 · 2023
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Composing parameter-efficient modules with arithmetic operation
Jinghan Zhang, Junteng Liu, Junxian He, et al · 2023
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Here’s a Free Lunch: Sanitizing Backdoored Models with Model Merge
Ansh Arora, Xuanli He, Maximilian Mozes, Srinibas Swain, Mark Dras, and Qiongkai Xu. 2024 · 2024
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SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
Jialu Li, Jaemin Cho, Yi-Lin Sung, Jaehong Yoon, and Mohit Bansal. 2024 · 2024
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MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, and Vishal M Patel. 2024 · 2024
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Task arithmetic in the tangent space: Improved editing of pre-trained models
Guillermo Ortiz-Jimenez, Alessandro Favero, and Pascal Frossard. 2024 · 2024
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Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Alexandre Rame, Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier, and Matthieu Cord. 2024 · 2024
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FuseChat: Knowledge Fusion of Chat Models
Fanqi Wan, Ziyi Yang, Longguang Zhong, Xiaojun Quan, Xinting Huang, and Wei Bi. 2024 · 2024
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Representation Surgery for Multi-Task Model Merging
Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, and Dacheng Tao. 2024 · 2024
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BapFL: You can Backdoor Personalized Federated Learning
Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, and Ming Gao. 2024 · 2024
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