What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Ran He mainly investigates Artificial intelligence, Facial recognition system, Pattern recognition, Face and Computer vision.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Iterative method and Machine learning.
His Facial recognition system research is multidisciplinary, incorporating elements of Representation, Contextual image classification, Linear discriminant analysis, Convolutional neural network and Pattern recognition.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Subspace topology and Robustness.
Ran He interconnects Feature, Feature learning, Wavelet transform and Image texture in the investigation of issues within Face.
His work in the fields of Face detection and Three-dimensional face recognition overlaps with other areas such as Euclidean space.
His most cited work include:
- Maximum Correntropy Criterion for Robust Face Recognition (508 citations)
- A Light CNN for Deep Face Representation With Noisy Labels (505 citations)
- Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis (323 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Facial recognition system, Face and Computer vision.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning.
His research in Pattern recognition intersects with topics in Subspace topology, Cluster analysis, Feature and Robustness.
His Robustness research incorporates themes from Optimization problem and Outlier.
His Facial recognition system research incorporates elements of Embedding, Convolutional neural network, Feature vector and Pattern recognition.
His study in the field of Face detection is also linked to topics like Identity.
He most often published in these fields:
- Artificial intelligence (87.33%)
- Pattern recognition (48.00%)
- Facial recognition system (31.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (87.33%)
- Pattern recognition (48.00%)
- Face (25.00%)
In recent papers he was focusing on the following fields of study:
Ran He focuses on Artificial intelligence, Pattern recognition, Face, Computer vision and Image.
The study of Artificial intelligence is intertwined with the study of Machine learning in a number of ways.
His studies in Pattern recognition integrate themes in fields like Generator and Autoencoder.
As a part of the same scientific study, he usually deals with the Face, concentrating on Structure and frequently concerns with DUAL.
His work on Image warping, Rendering and Monocular as part of general Computer vision study is frequently linked to Synchronization, therefore connecting diverse disciplines of science.
His work in Facial recognition system tackles topics such as Embedding which are related to areas like Matching.
Between 2019 and 2021, his most popular works were:
- Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition (18 citations)
- Deep Audio-Visual Learning: A Survey (16 citations)
- TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Ran He mostly deals with Artificial intelligence, Computer vision, Face, Deep learning and Facial recognition system.
His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence.
His work on Training set and Segmentation is typically connected to Distribution and Age estimation as part of general Pattern recognition study, connecting several disciplines of science.
He combines subjects such as Predicate and Transformer with his study of Computer vision.
His Face study integrates concerns from other disciplines, such as Structure, Speech recognition and Computation.
His Facial recognition system research is multidisciplinary, incorporating perspectives in Network architecture, Contextual image classification, Kernel and Kernel.
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