no code implementations • 21 May 2023 • Yijia Zhang, Lingran Zhao, Shijie Cao, WenQiang Wang, Ting Cao, Fan Yang, Mao Yang, Shanghang Zhang, Ningyi Xu
In this study, we conduct a comparative analysis of INT and FP quantization with the same bit-width, revealing that the optimal quantization format varies across different layers due to the complexity and diversity of tensor distribution.
no code implementations • CVPR 2023 • Yulu Gan, Mingjie Pan, Rongyu Zhang, Zijian Ling, Lingran Zhao, Jiaming Liu, Shanghang Zhang
To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model.
no code implementations • 14 Sep 2022 • Lingran Zhao, Zhen Dong, Kurt Keutzer
Quantization is wildly taken as a model compression technique, which obtains efficient models by converting floating-point weights and activations in the neural network into lower-bit integers.
no code implementations • CVPR 2021 • Yuchen Hong, Qian Zheng, Lingran Zhao, Xudong Jiang, Alex C. Kot, Boxin Shi
This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes.