Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy

15 November 2017

Papers citing "Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy"

9 / 59 papers shown

Title
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization K. Helwegen James Widdicombe Lukas Geiger Zechun Liu K. Cheng Roeland Nusselder MQ 16 110 0 05 Jun 2019
Training Quantized Neural Networks with a Full-precision Auxiliary Module Bohan Zhuang Lingqiao Liu Mingkui Tan Chunhua Shen Ian Reid MQ 24 62 0 27 Mar 2019
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation Bohan Zhuang Chunhua Shen Mingkui Tan Lingqiao Liu Ian Reid MQ 27 152 0 22 Nov 2018
Relaxed Quantization for Discretized Neural Networks Christos Louizos M. Reisser Tijmen Blankevoort E. Gavves Max Welling MQ 25 131 0 03 Oct 2018
Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation Zhezhi He Deliang Fan MQ 13 66 0 02 Oct 2018
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN) Jungwook Choi P. Chuang Zhuo Wang Swagath Venkataramani Vijayalakshmi Srinivasan K. Gopalakrishnan MQ 11 75 0 17 Jul 2018
Quantizing deep convolutional networks for efficient inference: A whitepaper Raghuraman Krishnamoorthi MQ 14 990 0 21 Jun 2018
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking Haichuan Yang Yuhao Zhu Ji Liu CVBM 12 36 0 12 Jun 2018
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights Aojun Zhou Anbang Yao Yiwen Guo Lin Xu Yurong Chen MQ 311 1,047 0 10 Feb 2017