J
Jiwen Lu
Researcher at Tsinghua University
Publications - 456
Citations - 22381
Jiwen Lu is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 66, co-authored 375 publications receiving 16276 citations. Previous affiliations of Jiwen Lu include Nanyang Technological University & Agency for Science, Technology and Research.
Papers
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Journal ArticleDOI
PCANet: A Simple Deep Learning Baseline for Image Classification?
TL;DR: PCANet as discussed by the authors is a simple deep learning network for image classification which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms.
Journal ArticleDOI
PCANet: A Simple Deep Learning Baseline for Image Classification?
TL;DR: Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)].
Proceedings ArticleDOI
Discriminative Deep Metric Learning for Face Verification in the Wild
Junlin Hu,Jiwen Lu,Yap-Peng Tan +2 more
TL;DR: The proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold.
Proceedings ArticleDOI
Deep hashing for compact binary codes learning
TL;DR: A deep neural network is developed to seek multiple hierarchical non-linear transformations to learn compact binary codes for large scale visual search and shows the superiority of the proposed approach over the state-of-the-arts.
Posted Content
A Siamese Long Short-Term Memory Architecture for Human Re-Identification
TL;DR: A novel siamese Long Short-Term Memory (LSTM) architecture that can process image regions sequentially and enhance the discriminative capability of local feature representation by leveraging contextual information.