What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Pattern recognition
Jiwen Lu mainly focuses on Artificial intelligence, Pattern recognition, Discriminative model, Feature extraction and Facial recognition system.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Metric and Computer vision.
His Pattern recognition research incorporates themes from Subspace topology, Feature, Artificial neural network, Binary code and Deep learning.
His work carried out in the field of Discriminative model brings together such families of science as Semi-supervised learning, Image and Face.
His Feature extraction study combines topics from a wide range of disciplines, such as Robustness and Feature vector.
His research in Facial recognition system intersects with topics in Histogram, Linear discriminant analysis, Principal component analysis and Sparse approximation.
His most cited work include:
- PCANet: A Simple Deep Learning Baseline for Image Classification? (690 citations)
- Discriminative Deep Metric Learning for Face Verification in the Wild (481 citations)
- Deep hashing for compact binary codes learning (406 citations)
What are the main themes of his work throughout his whole career to date?
Jiwen Lu spends much of his time researching Artificial intelligence, Pattern recognition, Feature extraction, Discriminative model and Computer vision.
His Artificial intelligence research includes themes of Machine learning and Metric.
The concepts of his Pattern recognition study are interwoven with issues in Subspace topology, Feature, Deep learning and Binary code.
Jiwen Lu interconnects Visualization, Histogram, Robustness and Biometrics in the investigation of issues within Feature extraction.
He combines subjects such as Gait, Image, Representation and Feature vector with his study of Discriminative model.
His biological study spans a wide range of topics, including Nonlinear dimensionality reduction and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (88.69%)
- Pattern recognition (58.72%)
- Feature extraction (31.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (88.69%)
- Pattern recognition (58.72%)
- Machine learning (22.94%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Theoretical computer science and Feature extraction.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
The concepts of his Pattern recognition study are interwoven with issues in Point cloud, Spectral clustering, Cluster analysis and Similarity.
His Theoretical computer science research is multidisciplinary, relying on both Graph, Graph based, Discriminative model and Graph.
The Discriminative model study combines topics in areas such as Feature and Feature learning.
His Feature extraction research incorporates themes from Artificial neural network, Semantics and Robustness.
Between 2019 and 2021, his most popular works were:
- Structure-Preserving Super Resolution With Gradient Guidance (25 citations)
- Deep Variational and Structural Hashing (18 citations)
- Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Feature extraction, Algorithm and Benchmark.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition.
His Pattern recognition research includes themes of False positive paradox and Pascal.
His work in Machine learning covers topics such as Metric which are related to areas like Generator, Decision boundary, Embedding and Training set.
His research integrates issues of Discriminative model and Robustness in his study of Feature extraction.
His Benchmark research incorporates elements of Binary code and Probabilistic logic.
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