Z
Zhaohui Yang
Researcher at Peking University
Publications - 26
Citations - 1150
Zhaohui Yang is an academic researcher from Peking University. The author has contributed to research in topics: Artificial neural network & Object detection. The author has an hindex of 11, co-authored 25 publications receiving 707 citations.
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Proceedings ArticleDOI
Data-Free Learning of Student Networks
Hanting Chen,Yunhe Wang,Chang Xu,Zhaohui Yang,Chuanjian Liu,Boxin Shi,Chunjing Xu,Chao Xu,Qi Tian +8 more
TL;DR: A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.
Proceedings ArticleDOI
Revisiting Perspective Information for Efficient Crowd Counting
TL;DR: Li et al. as mentioned in this paper proposed a perspective-aware convolutional neural network (PACNN) for efficient crowd counting, which integrates the perspective information into density regression to provide additional knowledge of the person scale change in an image.
Proceedings ArticleDOI
CARS: Continuous Evolution for Efficient Neural Architecture Search
TL;DR: In this paper, a continuous evolutionary approach for searching neural networks is proposed, where architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs.
Posted Content
CARS: Continuous Evolution for Efficient Neural Architecture Search
TL;DR: This work develops an efficient continuous evolutionary approach for searching neural networks that provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings and surpasses those produced by the state-of-the-art methods on the benchmark ImageNet dataset.
Proceedings ArticleDOI
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
TL;DR: This work proposes a hierarchical trinity search framework to simultaneously discover efficient architectures for all components of object detector in an end-to-end manner and empirically reveals that different parts of the detector prefer different operators.