Ning Huyan

Orcid: 0000-0002-6123-8659

According to our database1, Ning Huyan authored at least 14 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
AUD-Net: A Unified Deep Detector for Multiple Hyperspectral Image Anomaly Detection via Relation and Few-Shot Learning.
IEEE Trans. Neural Networks Learn. Syst., May, 2024

A Concurrent Multiscale Detector for End-to-End Image Matching.
IEEE Trans. Neural Networks Learn. Syst., March, 2024

Dual-Branch Residual Disentangled Adversarial Learning Network for Facial Expression Recognition.
IEEE Signal Process. Lett., 2024

2022
Element-Wise Feature Relation Learning Network for Cross-Spectral Image Patch Matching.
IEEE Trans. Neural Networks Learn. Syst., 2022

Unsupervised Outlier Detection Using Memory and Contrastive Learning.
IEEE Trans. Image Process., 2022

Cluster-Memory Augmented Deep Autoencoder via Optimal Transportation for Hyperspectral Anomaly Detection.
IEEE Trans. Geosci. Remote. Sens., 2022

Background Representation Learning With Structural Constraint for Hyperspectral Anomaly Detection.
IEEE Geosci. Remote. Sens. Lett., 2022

2021
Multi-Relation Attention Network for Image Patch Matching.
IEEE Trans. Image Process., 2021

Spectral-Difference Low-Rank Representation Learning for Hyperspectral Anomaly Detection.
IEEE Trans. Geosci. Remote. Sens., 2021

2019
Hyperspectral Anomaly Detection via Background and Potential Anomaly Dictionaries Construction.
IEEE Trans. Geosci. Remote. Sens., 2019

AFD-Net: Aggregated Feature Difference Learning for Cross-Spectral Image Patch Matching.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018
Spatial-Spectral Graph-Based Nonlinear Embedding Dimensionality Reduction for Hyperspectral Image Classificaiton.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

Hyper-Laplacian Regularized Low-Rank Tensor Decomposition for Hyperspectral Anomaly Detection.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

2017
Recursive Autoencoders-Based Unsupervised Feature Learning for Hyperspectral Image Classification.
IEEE Geosci. Remote. Sens. Lett., 2017


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