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Baochang Zhang
Researcher at Beihang University
Publications - 363
Citations - 9354
Baochang Zhang is an academic researcher from Beihang University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 40, co-authored 294 publications receiving 6433 citations. Previous affiliations of Baochang Zhang include Istituto Italiano di Tecnologia & Nanchang Institute of Technology.
Papers
More filters
Journal ArticleDOI
Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor
TL;DR: The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP).
Journal ArticleDOI
Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition
TL;DR: The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate.
Proceedings ArticleDOI
HRank: Filter Pruning Using High-Rank Feature Map
TL;DR: This paper proposes a novel filter pruning method by exploring the High Rank of feature maps (HRank), inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive.
Proceedings ArticleDOI
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Shaohui Lin,Rongrong Ji,Chenqian Yan,Baochang Zhang,Liujuan Cao,Qixiang Ye,Feiyue Huang,David Doermann +7 more
TL;DR: This paper proposes an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner and effectively solves the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end to end manner.
Journal ArticleDOI
Learning Compact and Discriminative Stacked Autoencoder for Hyperspectral Image Classification
TL;DR: The proposed CDSAE framework comprises two stages with different optimization objectives, which can learn discriminative low-dimensional feature mappings and train an effective classifier progressively, and imposes a local Fisher discriminant regularization on each hidden layer of stacked autoencoder (SAE) to train discrim inative SAE (DSAE).