Name: Lei Huang
Type: User
Company: Beihang University, Associate Professor
Bio: Associate professor in BeiHang University, research interest: deep learning, semi-supervised learning, active learning and their application to visual dada
Location: Beijing, China
Blog: https://huangleibuaa.github.io/
Lei Huang's Projects
This is a project of my paper related to active learning and graph based semi-supervised learning, which is published on ICME 2014. The project is written by matlab
This project is the Torch implementation of our ICCV 2017 paper: Centered Weight Normalization in Accelerating Training of Deep Neural Networks
Website & blog
An implementation of DecorrelatedBN by tensorflow
This project is the PyTorch implementation of our accepted CVPR 2021 paper, Group Whitening: Balancing Learning Efficiency and Representational Capacity
This project is the Torch implementation of our accepted CVPR 2019 paper, Iterative Normalization: Beyond Standardization towards Efficient Whitening
This is the pytorch re-implementation of the IterNorm
The reimplementation of Ladder networks with projection based weight normalization. We achieved test errors as 2.52%, 1.06%, and 0.91% on Permunate invariant MNIST dataset with 20, 50, and 100 labeled samples respectively, which is the state-of-the-art results.
This project is for the paper: Exploring learning dynamics of DNNs via layerwise conditioning analysis
This code is for our ICML 2020 paper "On the Number of Linear Regions of Convolutional Neural Networks."
Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
This repo is for our paper: Normalization Techniques in Training DNNs: Methodology, Analysis and Application
This project is the Torch implementation of the paper: Projection Based Weight Normalization for Deep Neural Networks (arXiv:1710.02338)
PyTorch and Torch implementation for our accepted CVPR 2020 paper (Oral): Controllable Orthogonalization in Training DNNs
This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonality constraints over Steifel manifold in deep neural networks (arXiv:1709.06079 )
PyTorch and Tensorflow implementation for our accepted CVPR 2020 paper (Oral): An Investigation into the Stochasticity of Batch Whitening
A Framework of Small-scale Large Multimodal Models
This project is the PyTorch implementation of our CVPR 2022 paper: