20 citations · 30 across the 5 of their papers we have counts for
7 papers
Incorporating Convolution Designs into Visual Transformers
Kun Yuan, Shaopeng Guo, Ziwei Liu +3
Motivated by the success of Transformers in natural language processing (NLP) tasks, there emerge some attempts (e.g., ViT and DeiT) to apply Transformers to the vision domain. How…
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Yuhang Li, Ruihao Gong, Xu Tan +6
We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training…
Once Quantization-Aware Training: High Performance Extremely Low-bit Architecture Search
Mingzhu Shen, Feng Liang, Ruihao Gong +6
Quantization Neural Networks (QNN) have attracted a lot of attention due to their high efficiency. To enhance the quantization accuracy, prior works mainly focus on designing advan…
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Yuhang Li, Wei Wang, Haoli Bai +3
Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activati…
Towards Unified INT8 Training for Convolutional Neural Network
Feng Zhu, Ruihao Gong, Fengwei Yu +5
Recently low-bit (e.g., 8-bit) network quantization has been extensively studied to accelerate the inference. Besides inference, low-bit training with quantized gradients can furth…
Forward and Backward Information Retention for Accurate Binary Neural Networks
Haotong Qin, Ruihao Gong, Xianglong Liu +4
Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many bina…