5 citations · 9 across the 5 of their papers we have counts for
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cs.CV2022
Exploiting the Partly Scratch-off Lottery Ticket for Quantization-Aware Training
Yunshan Zhong, Gongrui Nan, Yuxin Zhang +2
Quantization-aware training (QAT) receives extensive popularity as it well retains the performance of quantized networks. In QAT, the contemporary experience is that all quantized…
cs.CV2022★ 5 cited
LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal
Hong Yang, Gongrui Nan, Mingbao Lin +4
This paper focuses on the limitations of current over-parameterized shadow removal models. We present a novel lightweight deep neural network that processes shadow images in the LA…
cs.CV2021★ 4 cited
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
Yunshan Zhong, Mingbao Lin, Gongrui Nan +4
Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of the r…