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Task Arithmetic has emerged as a simple yet effective method to merge models without additional training.
The approximation of one matrix by another of lower rank
Carl Eckart and G. Marion Young · 1936
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Ng, and Honglak Lee · 1938
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Note on a method for calculating corrected sums of squares and products
B. P. Welford · 1962
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Automated Flower Classification over a Large Number of Classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Tensor decompositions and applications
Tamara G Kolda and Brett W Bader · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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The German Traffic Sign Recognition Benchmark: A multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando De Freitas · 2013
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Challenges in Representation Learning: A Report on Three Machine Learning Contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, and Yoshua Bengio · 2013
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3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Food-101 – Mining Discriminative Components with Random Forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing Textures in the Wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever · 2014
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The optimal hard threshold for singular values is
Matan Gavish and David L. Donoho · 2014
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Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
Cited alongside, same era.
Ultimate tensorization: compressing convolutional and fc layers alike
Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, and Dmitry Vetrov · 2016
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Compression of deep convolutional neural networks for fast and low power mobile applications
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin · 2016
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SUN Database: Exploring a Large Collection of Scene Categories
Jianxiong Xiao, Krista A. Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
Cited alongside, same era.
Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
Later among the works it cites.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2023
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Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng · 2023
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REPAIR: REnormalizing permuted activations for interpolation repair
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, and Behnam Neyshabur · 2023
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Re-basin via implicit sinkhorn differentiation
Fidel A Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail, Masih Aminbeidokhti, Eric Granger, and Marco Pedersoli · 2023
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Model merging by uncertainty-based gradient matching
Nico Daheim, Thomas Möllenhoff, Edoardo Ponti, Iryna Gurevych, and Mohammad Emtiyaz Khan · 2024
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Remote Sensing Image Scene Classification: Benchmark and State of the Art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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EMNIST: Extending MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, *Azalia Mirhoseini, *Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Deep learning for classical japanese literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha · 2018
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Rotation Equivariant CNNs for Digital Pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Closest in time.
EMR-merging: Tuning-free high-performance model merging
Chenyu Huang, Peng Ye, Tao Chen, Tong He, Xiangyu Yue, and Wanli Ouyang · 2024
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Twin-merging: Dynamic integration of modular expertise in model merging
Zhenyi Lu, Chenghao Fan, Wei Wei, Xiaoye Qu, Dangyang Chen, and Yu Cheng · 2024
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Efficient and effective weight-ensembling mixture of experts for multi-task model merging
Li Shen, Anke Tang, Enneng Yang, Guibing Guo, Yong Luo, Lefei Zhang, Xiaochun Cao, Bo Du, and Dacheng Tao · 2024
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Zipit! merging models from different tasks without training
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Smile: Zero-shot sparse mixture of low-rank experts construction from pre-trained foundation models
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Localizing task information for improved model merging and compression
Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez, François Fleuret, and Pascal Frossard · 2024
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Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal · 2024
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Adamerging: Adaptive model merging for multi-task learning
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li · 2024
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Atm: Improving model merging by alternating tuning and merging, 2024
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c 2 m 3 c^{2}m^{3} : Cycle-consistent multi-model merging
Donato Crisostomi, Marco Fumero, Daniele Baieri, Florian Bernard, and Emanuele Rodolà · 2025
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Model breadcrumbs: Scaling multi-task model merging with sparse masks
MohammadReza Davari and Eugene Belilovsky · 2025
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