dengwanxia1991

vat_tf

Virtual adversarial training with Tensorflow

pytorch-cifar100

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)

SENet-PyTorch

UCDAN

Tensorflow implementation of UCDAN

UODTN

Codes for UODTN; Open domain recognition; CVPR19

DeepCORAL

🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

metric-transfer.pytorch

Deep Metric Transfer for Label Propagation with Limited Annotated Data

self-supervised-da

self-supervised domain adaptation

Pix2Vox

The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)

graph_nn

Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

3DKeypoints-DA

Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

residual_adapters

deepcluster

Deep Clustering for Unsupervised Learning of Visual Features

DeepClustering

Methods and Implements of Deep Clustering

gmm_image_denoising

Image Denoising Using Unsupervised Learning of Different Gaussian Mixture Models

unsup-GMM

From scratch for fun: Gaussian Mixture Models for unsupervised learning

Unsupervised-Features-Learning-for-Image-Classification

Recently, image classification draw attentions of many researchers. The need of object recognition grows drastically, especially in the context of biometric, biomedical imaging and real time scene understanding. Computer vision task is the most challenging in machine learning. For that reason, it's fundamental to tackle this concern using appropriate clustering and classification techniques. However, the quest for the best unsupervised features extraction remain an open problem even if CNNs reach a remarkable success, establishing new state-of-the-art. In this context, we study from an acute insight standpoint the standard clustering models K-means, GMM and Naive Bayes classification algorithm in order to draw conclusion and underline their limits for such complicated tasks. To what extent are k-means and GMM efficient ? Why they fail and how to circumvent their weaknesses.

Learning-via-Translation

SPGAN in CVPR'18

ETN

Code released for CVPR 2019 paper "Learning to Transfer Examples for Partial Domain Adaptation"

MDD

Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".

pytorch-atda

A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation

ATDA

Unofficial Implement of Asymmetric Tri-training for Unsupervised Domain Adaptation

Generate_To_Adapt

Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"

Deformable-Convolution-V2-PyTorch

Deformable ConvNets V2 (DCNv2) in PyTorch

Deformable_ConvNet_pytorch

Pytorch implementation of Deformable Convolutional Network

mmdetection

OpenMMLab Detection Toolbox and Benchmark

visda-2019-public

visda-2018-public

bgm

Code for "Boosted Generative Models", AAAI 2018.