I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization

16 November 2023

Papers citing "I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization"

8 / 8 papers shown

Title
Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Changjun Li Runqing Jiang Zhuo Song Pengpeng Yu Ye Zhang Yulan Guo MQ 43 0 0 01 May 2025
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers Yunshan Zhong Yuyao Zhou Yuxin Zhang Shen Li Yong Li Fei Chao Zhanpeng Zeng Rongrong Ji MQ 83 0 0 31 Dec 2024
Towards Accurate Post-Training Quantization for Vision Transformer Yifu Ding Haotong Qin Qing-Yu Yan Z. Chai Junjie Liu Xiaolin K. Wei Xianglong Liu MQ 47 66 0 25 Mar 2023
I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference Zhikai Li Qingyi Gu MQ 41 94 0 04 Jul 2022
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer Sachin Mehta Mohammad Rastegari ViT 181 1,148 0 05 Oct 2021
Fine-grained Data Distribution Alignment for Post-Training Quantization Yunshan Zhong Mingbao Lin Mengzhao Chen Ke Li Yunhang Shen Fei Chao Yongjian Wu Rongrong Ji MQ 37 18 0 09 Sep 2021
BinaryBERT: Pushing the Limit of BERT Quantization Haoli Bai Wei Zhang Lu Hou Lifeng Shang Jing Jin Xin Jiang Qun Liu Michael Lyu Irwin King MQ 138 183 0 31 Dec 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 279 39,083 0 01 Sep 2014