FTL: A universal framework for training low-bit DNNs via Feature Transfer

no code implementations • ECCV 2020 • Kunyuan Du, Ya zhang, Haibing Guan, Qi Tian, Shenggan Cheng, James Lin

Compared with low-bit models trained directly, the proposed framework brings 0. 5% to 3. 4% accuracy gains to three different quantization schemes.

Quantization Transfer Learning

From Quantized DNNs to Quantizable DNNs

no code implementations • 11 Apr 2020 • Kunyuan Du, Ya zhang, Haibing Guan

This paper proposes Quantizable DNNs, a special type of DNNs that can flexibly quantize its bit-width (denoted as `bit modes' thereafter) during execution without further re-training.