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The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices.
Quantizing for maximum output entropy (corresp.)
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Imagenet classification with deep convolutional neural networks
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Estimating or propagating gradients through stochastic neurons for conditional computation
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Attention is all you need
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2017
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Soft filter pruning for accelerating deep convolutional neural networks
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Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, and Ian Reid · 2018
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Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha · 2019
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Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Post-training quantization for vision transformer
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Do vision transformers see like convolutional neural networks?
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
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Post-training piecewise linear quantization for deep neural networks
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
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