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Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
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 · 2010
Earlier work this paper cites.
Caltech-ucsd birds 200
Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona · 2010
Earlier work this paper cites.
Bag-of-visual-words and spatial extensions for land-use classification
Yi Yang and Shawn Newsam · 2010
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Improving deep neural networks for lvcsr using rectified linear units and dropout
George E Dahl, Tara N Sainath, and Geoffrey E Hinton · 2013
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.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Cited alongside, same era.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Cited alongside, same era.
Collection of textures in colorectal cancer histology
Jakob Nikolas Kather, Frank Gerrit Zöllner, Francesco Bianconi, Susanne M Melchers, Lothar R Schad, Timo Gaiser, Alexander Marx, and Cleo-Aron Weis · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Better plain vit baselines for imagenet-1k
Lucas Beyer, Xiaohua Zhai, and Alexander Kolesnikov · 2022
Later among the works it cites.
Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
Later among the works it cites.
Transformer quality in linear time
Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le · 2022
Later among the works it cites.
Sima: Simple softmax-free attention for vision transformers
Soroush Abbasi Koohpayegani and Hamed Pirsiavash · 2022
Later among the works it cites.
Robust training of neural networks using scale invariant architectures
Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank Reddi, and Sanjiv Kumar · 2022
Later among the works it cites.
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Infinite attention: Nngp and ntk for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, and Roman Novak · 2020
Cited alongside, same era.
Transformers are RNNs: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
Cited alongside, same era.
Soft: Softmax-free transformer with linear complexity
Jiachen Lu, Jinghan Yao, Junge Zhang, Xiatian Zhu, Hang Xu, Weiguo Gao, Chunjing Xu, Tao Xiang, and Li Zhang · 2021
Cited alongside, same era.
Markus N Rabe and Charles Staats · 2021
Cited alongside, same era.
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
Closest in time.
Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, et al · 2023
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What can a single attention layer learn? a study through the random features lens
Hengyu Fu, Tianyu Guo, Yu Bai, and Song Mei · 2023
Closest in time.
A study on relu and softmax in transformer
Kai Shen, Junliang Guo, Xu Tan, Siliang Tang, Rui Wang, and Jiang Bian · 2023
Closest in time.