1 code implementation • 25 Feb 2024 • Luoming Zhang, Yefei He, Wen Fei, Zhenyu Lou, Weijia Wu, YangWei Ying, Hong Zhou
Our framework outperforms previous methods by approximately 1\% for 8-bit PTQ and 2\% for 6-bit PTQ, showcasing its superior performance.
no code implementations • 7 Oct 2023 • Luoming Zhang, Wen Fei, Weijia Wu, Yefei He, Zhenyu Lou, Hong Zhou
Fine-grained quantization has smaller quantization loss, consequently achieving superior performance.
no code implementations • ICCV 2023 • Yefei He, Zhenyu Lou, Luoming Zhang, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
no code implementations • 14 Nov 2022 • Yefei He, Zhenyu Lou, Luoming Zhang, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang
To solve this, we propose Softmax-aware Binarization, which dynamically adapts to the data distribution and reduces the error caused by binarization.
no code implementations • 16 May 2022 • Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
Extensive experiments demonstrate that the proposed method yields surprising performance both in image classification and human pose estimation tasks.
Ranked #1 on Binarization on ImageNet (Top 1 Accuracy metric)
no code implementations • 8 Apr 2022 • Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization.