no code implementations • 24 May 2023 • Junrui Xiao, Zhikai Li, Lianwei Yang, Qingyi Gu
In this paper, we first argue empirically that the severe performance degradation is mainly caused by the weight oscillation in the binarization training and the information distortion in the activation of ViTs.
no code implementations • 11 May 2023 • Junrui Xiao, Zhikai Li, Lianwei Yang, Qingyi Gu
As emerging hardware begins to support mixed bit-width arithmetic computation, mixed-precision quantization is widely used to reduce the complexity of neural networks.
1 code implementation • ICCV 2023 • Zhikai Li, Junrui Xiao, Lianwei Yang, Qingyi Gu
Post-training quantization (PTQ), which only requires a tiny dataset for calibration without end-to-end retraining, is a light and practical model compression technique.