What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Quantum mechanics
- Machine learning
Yi Yang focuses on Artificial intelligence, Pattern recognition, Machine learning, Convolutional neural network and Discriminative model.
Artificial intelligence is closely attributed to Computer vision in his study.
His work carried out in the field of Pattern recognition brings together such families of science as Object detection, Similarity, Feature and Image retrieval.
His Machine learning research focuses on TRECVID and how it relates to Leverage.
As a part of the same scientific family, Yi Yang mostly works in the field of Convolutional neural network, focusing on Training set and, on occasion, Regularization.
His Discriminative model research integrates issues from Embedding, Feature learning, Outlier and Pooling.
His most cited work include:
- Articulated pose estimation with flexible mixtures-of-parts (827 citations)
- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro (775 citations)
- Attention to Scale: Scale-Aware Semantic Image Segmentation (766 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Optoelectronics, Machine learning and Discriminative model are his primary areas of study.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
His work deals with themes such as Laser, Optics and Graphene, which intersect with Optoelectronics.
His Graphene study is focused on Nanotechnology in general.
He most often published in these fields:
- Artificial intelligence (34.41%)
- Pattern recognition (14.84%)
- Optoelectronics (14.77%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (34.41%)
- Pattern recognition (14.84%)
- Machine learning (12.29%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Segmentation.
His Artificial intelligence research is multidisciplinary, incorporating elements of Domain, Computer vision and Code.
Yi Yang has researched Pattern recognition in several fields, including Object detection and Representation.
His Machine learning study which covers Graph that intersects with Graph.
His research is interdisciplinary, bridging the disciplines of Feature learning and Discriminative model.
His studies deal with areas such as Object and Embedding as well as Feature.
Between 2019 and 2021, his most popular works were:
- Random Erasing Data Augmentation (214 citations)
- NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search (90 citations)
- Deep Unfolded Robust PCA With Application to Clutter Suppression in Ultrasound (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Machine learning
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Segmentation.
Yi Yang has included themes like Domain and Code in his Artificial intelligence study.
His research in Pattern recognition tackles topics such as Process which are related to areas like Ground truth and Benchmark.
His Machine learning study combines topics in areas such as Adversarial system, Graph and Task.
His Discriminative model research incorporates themes from Parsing, Representation and Feature learning.
As a member of one scientific family, Yi Yang mostly works in the field of Convolutional neural network, focusing on Contextual image classification and, on occasion, Object detection and Pruning.
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