Kun FANG

About

My research interests focus on trustworthy AI, mainly on the adversarial and certified robustness of deep neural networks, and the topic of out-of-distribution detection. I also have previously dabbled in the kernel methods in machine learning.

Work

Hong Kong Polytechnic University

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Postdoctoral Fellow

Hong Kong

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Education

Shanghai Jiao Tong University

China
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Ph.D.

Shanghai Jiao Tong University

China
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Master

Tongji University

China
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Bachelor

Publications

Multi-head ensemble of smoothed classifiers for certified robustness

Neural Networks

journal-article

Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective

International Journal of Computer Vision

journal-article

Towards robust neural networks via orthogonal diversity

Pattern Recognition

journal-article

Kernel PCA for Out-of-Distribution Detection

Advances in Neural Information Processing Systems

conference-paper

Unifying Gradients to Improve Real-World Robustness for Deep Networks

ACM Transactions on Intelligent Systems and Technology

journal-article

Improving the adversarial robustness of quantized neural networks via exploiting the feature diversity

Pattern Recognition Letters

journal-article

Improving adversarial robustness through a curriculum-guided reliable distillation

Computers & Security

journal-article

Local-Global Semantic Fusion Single-shot Classification Method

International Conference on Neural Information Processing

conference-paper

End-to-end kernel learning via generative random Fourier features

Pattern Recognition

journal-article

CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

conference-paper

Subspace Adversarial Training

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

conference-paper

ADVMIX: Data Augmentation for Accurate Scene Text Spotting

2021 IEEE International Conference on Image Processing (ICIP)

conference-paper

Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels

2021 International Joint Conference on Neural Networks (IJCNN)

conference-paper

Kun FANG