8 citations · 14 across the 4 of their papers we have counts for
4 papers
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization
Peng Hu, Xi Peng, Hongyuan Zhu +2
As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hard…
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery
Tianyi Zhang, Jie Lin, Peng Hu +2
Non-maximum Suppression (NMS) is an essential postprocessing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, t…
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition
Govind Narasimman, Kangkang Lu, Arun Raja +4
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised…
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
Xue Geng, Jie Fu, Bin Zhao +4
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.…