huangleiBuaa/IterNorm-pytorch

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huangleiBuaa/IterNorm-pytorch

huangleiBuaa

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This is the pytorch re-implementation of the IterNorm

NormalizationSurvey

This repo is for our paper: Normalization Techniques in Training DNNs: Methodology, Analysis and Application

OthogonalWN

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 )

XBNBlock

This project is the PyTorch implementation of our CVPR 2022 paper:

IterNorm

This project is the Torch implementation of our accepted CVPR 2019 paper, Iterative Normalization: Beyond Standardization towards Efficient Whitening

ONI

PyTorch and Torch implementation for our accepted CVPR 2020 paper (Oral): Controllable Orthogonalization in Training DNNs

CenteredWN

This project is the Torch implementation of our ICCV 2017 paper: Centered Weight Normalization in Accelerating Training of Deep Neural Networks

LRCount-CNN

This code is for our ICML 2020 paper "On the Number of Linear Regions of Convolutional Neural Networks."

StochasticityBW

PyTorch and Tensorflow implementation for our accepted CVPR 2020 paper (Oral): An Investigation into the Stochasticity of Batch Whitening

GroupWhitening

This project is the PyTorch implementation of our accepted CVPR 2021 paper, Group Whitening: Balancing Learning Efficiency and Representational Capacity

NormProjection

This project is the Torch implementation of the paper: Projection Based Weight Normalization for Deep Neural Networks (arXiv:1710.02338)

Active-GBSSL

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

LayerwiseCA

This project is for the paper: Exploring learning dynamics of DNNs via layerwise conditioning analysis

Ladder_deepSSL_NP

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.

huangleiBuaa.github.io