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Vision transformers (ViTs) have demonstrated remarkable performance across various visual tasks.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
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Hawq: Hessian aware quantization of neural networks with mixed-precision
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2019
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Understanding individual decisions of cnns via contrastive backpropagation
Jindong Gu, Yinchong Yang, and Volker Tresp · 2019
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Full-gradient representation for neural network visualization
Suraj Srinivas and François Fleuret · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
Cited alongside, same era.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
Cited alongside, same era.
Autoq: Automated kernel-wise neural network quantization
Qian Lou, Feng Guo, Minje Kim, Lantao Liu, and Lei Jiang · 2020
Cited alongside, same era.
Q-bert: Hessian based ultra low precision quantization of bert
Sheng Shen, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
Cited alongside, same era.
Dearkd: data-efficient early knowledge distillation for vision transformers
Xianing Chen, Qiong Cao, Yujie Zhong, Jing Zhang, Shenghua Gao, and Dacheng Tao · 2022
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Towards accurate post-training quantization for vision transformer
Yifu Ding, Haotong Qin, Qinghua Yan, Zhenhua Chai, Junjie Liu, Xiaolin Wei, and Xianglong Liu · 2022
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Exploring plain vision transformer backbones for object detection
Yanghao Li Hanzi Mao, Ross Girshick, and Kaiming He · 2022
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Tinyvit: Fast pretraining distillation for small vision transformers
Kan Wu, Jinnian Zhang, Houwen Peng, Mengchen Liu, Bin Xiao, Jianlong Fu, and Lu Yuan · 2022
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Width & depth pruning for vision transformers
Fang Yu, Kun Huang, Meng Wang, Yuan Cheng, Wei Chu, and Li Cui · 2022
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Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization
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Zhigang Dai, Bolun Cai, Yugeng Lin, and Junying Chen · 2021
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, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
Cited alongside, same era.
Segmenter: Transformer for semantic segmentation
Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid · 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.
Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
Cited alongside, same era.
Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al · 2021
Cited alongside, same era.
Zhihang Yuan, Chenhao Xue, Yiqi Chen, Qiang Wu, and Guangyu Sun · 2022
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Minivit: Compressing vision transformers with weight multiplexing
Jinnian Zhang, Houwen Peng, Kan Wu, Mengchen Liu, Bin Xiao, Jianlong Fu, and Lu Yuan · 2022
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Oneformer: One transformer to rule universal image segmentation
Jitesh Jain, Jiachen Li, Mang Tik Chiu, Ali Hassani, Nikita Orlov, and Humphrey Shi · 2023
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I-vit: Integer-only quantization for efficient vision transformer inference
Zhikai Li and Qingyi Gu · 2023
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Repq-vit: Scale reparameterization for post-training quantization of vision transformers
Zhikai Li, Junrui Xiao, Lianwei Yang, and Qingyi Gu · 2023
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Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers
Yijiang Liu, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, and Shanghang Zhang · 2023
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Patch-wise mixed-precision quantization of vision transformer
Junrui Xiao, Zhikai Li, Lianwei Yang, and Qingyi Gu · 2023
Later among the works it cites.