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Model merging aims to efficiently combine the weights of multiple expert models, each trained on a specific task, into a single multi-task model, with strong performance across all tasks.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Novel dataset for fine-grained image categorization
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei · 2011
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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 Ng · 2011
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Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
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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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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Collection of textures in colorectal cancer histology, June 2016
Jakob Nikolas Kather, Frank Gerrit Zöllner, Francesco Bianconi, Susanne M Melchers, Lothar R Schad, Timo Gaiser, Alexander Marx, and Cleo-Aron Weis · 2016
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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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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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Cassava disease classification, 2019
Timnit Gebru ErnestMwebaze · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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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
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Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J. Zico Kolter · 2019
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Linear mode connectivity and the lottery ticket hypothesis
Imagenet-21k pretraining for the masses, 2021
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
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The role of permutation invariance in linear mode connectivity of neural networks
Rahim Entezari, Hanie Sedghi, Olga Saukh, and Behnam Neyshabur · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 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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Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
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Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi · 2020
Cited alongside, same era.
Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
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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
Cited alongside, same era.
Later among the works it cites.
TIES-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
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Simultaneous linear connectivity of neural networks modulo permutation
Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, and Gintare Karolina Dziugaite · 2024
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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
Closest in time.