Pinned Repositories
3dUnet
AAAI21_Are_Adversarial_Examples_Created_Equal
ACDC2017
AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
ASONAM22_COVID_Contrastive_Adversarial_Domain_Mixup
convex_adversarial
A method for training neural networks that are provably robust to adversarial attacks.
cord19
Get started with CORD-19
deterministic-uncertainty-quantification
Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
IJCAI22_Attacking_Out_Domain_Uncertainty
huiminzeng's Repositories
huiminzeng/IJCAI22_Attacking_Out_Domain_Uncertainty
huiminzeng/AAAI21_Are_Adversarial_Examples_Created_Equal
huiminzeng/3dUnet
huiminzeng/ACDC2017
huiminzeng/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
huiminzeng/ASONAM22_COVID_Contrastive_Adversarial_Domain_Mixup
huiminzeng/convex_adversarial
A method for training neural networks that are provably robust to adversarial attacks.
huiminzeng/cord19
Get started with CORD-19
huiminzeng/deterministic-uncertainty-quantification
Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
huiminzeng/DUE
Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".
huiminzeng/Folder-Structure-Conventions
Folder / directory structure options and naming conventions for software projects
huiminzeng/huiminzeng.github.io
Personal Webpage
huiminzeng/python
Show Me the Code Python version.
huiminzeng/PyTorchDL_GTA5
This is the repository for loading GTA5 Dataset with PyTorch
huiminzeng/Shape-Completion
huiminzeng/TRADES
TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)
huiminzeng/VirtualScan
virtual scanner
huiminzeng/vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
huiminzeng/YOPO-You-Only-Propagate-Once
Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle