10 citations · 32 across the 13 of their papers we have counts for
13 papers
Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Model
Kun Fang, Zuopeng Yang, Haibo Hu +3
Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or fro…
Revisiting Random Weight Perturbation for Efficiently Improving Generalization
Tao Li, Qinghua Tao, Weihao Yan +5
Improving the generalization ability of modern deep neural networks (DNNs) is a fundamental challenge in machine learning. Two branches of methods have been proposed to seek flat m…
Kernel PCA for Out-of-Distribution Detection
Kun Fang, Qinghua Tao, Kexin Lv +3
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs). Existing works have shown the insufficiency of Principal Component Analysis (PCA) s…
Revisiting Deep Ensemble for Out-of-Distribution Detection: A Loss Landscape Perspective
Kun Fang, Qinghua Tao, Xiaolin Huang +1
Existing Out-of-Distribution (OoD) detection methods address to detect OoD samples from In-Distribution (InD) data mainly by exploring differences in features, logits and gradients…
Efficient Generalization Improvement Guided by Random Weight Perturbation
Tao Li, Weihao Yan, Zehao Lei +4
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability. Recently, sharpn…
Multi-head Ensemble of Smoothed Classifiers for Certified Robustness
Kun Fang, Qinghua Tao, Yingwen Wu +3
Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple Deep Neural Networks (DNNs) has shown state-of-the-art perf…
Online LiDAR-Camera Extrinsic Parameters Self-checking
Pengjin Wei, Guohang Yan, Yikang Li +3
With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This…
Unifying Gradients to Improve Real-world Robustness for Deep Networks
Yingwen Wu, Sizhe Chen, Kun Fang +1
The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial atta…
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene
Pengjin Wei, Guohang Yan, Yikang Li +4
Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent sys…
Subspace Adversarial Training
Tao Li, Yingwen Wu, Sizhe Chen +2
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However, a serious problem of catastrophic overfitting exists, i.e.,…
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Qin Luo, Kun Fang, Jie Yang +1
Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite.…
Towards Robust Neural Networks via Orthogonal Diversity
Kun Fang, Qinghua Tao, Yingwen Wu +5
Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs.…
End-to-end Kernel Learning via Generative Random Fourier Features
Kun Fang, Fanghui Liu, Xiaolin Huang +1
Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the fi…