Fetching the paper…
Reading the bibliography…
The increasing scale of vision transformers (ViT) has made the efficient fine-tuning of these large models for specific needs a significant challenge in various applications.
Combinatorial Mathematics
Herbert John Ryser · 1963
Earlier work this paper cites.
Computation of the fast walsh-fourier transform
J.L. Shanks · 1969
Earlier work this paper cites.
Digital Image Processing (2nd Ed.)
Rafael C. Gonzales and Paul Wintz · 1987
Earlier work this paper cites.
A Visual Vocabulary for Flower Classification
Maria-Elena Nilsback and Andrew Zisserman · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Cats and Dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
Earlier work this paper cites.
3D Object Representations for Fine-grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Food-101 – Mining Discriminative Components with Random Forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
How Transferable are Features in Deep Neural Networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Learning Transferable Features with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
Cited alongside, same era.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
Cited alongside, same era.
Scalable methods for 8-bit training of neural networks
Ron Banner, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2018
Cited alongside, same era.
Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
Later among the works it cites.
Distribution Adaptive INT8 Quantization for Training CNNs
Kang Zhao, Sida Huang, Pan Pan, Yinghan Li, Yingya Zhang, Zhenyu Gu, and Yinghui Xu · 2021
Later among the works it cites.
Swin Transformer v2: Scaling up Capacity and Resolution
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al · 2022
Later among the works it cites.
Monovit: Self-supervised monocular depth estimation with a vision transformer
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi, Fabio Tosi, Xianda Guo, Zheng Zhu, Guan Huang, Yang Tang, and Stefano Mattoccia · 2022
Later among the works it cites.
Ashutosh Agarwal and Chetan Arora · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Spottune: Transfer Learning through Adaptive Fine-tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, and Rogerio Feris · 2019
Cited alongside, same era.
Tinytl: Reduce Memory, not Parameters for Efficient On-device Learning
Han Cai, Chuang Gan, Ligeng Zhu, and Song Han · 2020
Cited alongside, same era.
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Jianfei Chen, Yu Gai, Zhewei Yao, Michael W Mahoney, and Joseph E Gonzalez · 2020
Cited alongside, same era.
Ultra-Low Precision 4-bit Training of Deep Neural Networks
Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi (Viji) Srinivasan, and Kailash Gopalakrishnan · 2020
Cited alongside, same era.
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, et al · 2021
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Cited alongside, same era.
A Survey on Federated Learning
Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao · 2021
Cited alongside, same era.
Anytime Depth Estimation with Limited Sensing and Computation Capabilities on Mobile Devices
Yuedong Yang, Zihui Xue, and Radu Marculescu · 2022
Later among the works it cites.
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
Later among the works it cites.
On-device Training under 256kb Memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song Han · 2022
Later among the works it cites.
Efficient-grad: Efficient training deep convolutional neural networks on edge devices with grad ient optimizations
Ziyang Hong and C Patrick Yue · 2022
Later among the works it cites.
Lst: Ladder Side-tuning for Parameter and Memory Efficient Transfer Learning
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
Later among the works it cites.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Closest in time.
Vision transformer for nerf-based view synthesis from a single input image
Kai-En Lin, Yen-Chen Lin, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi · 2023
Closest in time.
Efficient On-device Training via Gradient Filtering
Yuedong Yang, Guihong Li, and Radu Marculescu · 2023
Closest in time.