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Title: transferlearning/code at master · Array9155/transferlearning · GitHub

Open Graph Title: transferlearning/code at master · Array9155/transferlearning

X Title: transferlearning/code at master · Array9155/transferlearning

Description: Everything about Transfer Learning and Domain Adaptation--迁移学习 - transferlearning/code at master · Array9155/transferlearning

Open Graph Description: Everything about Transfer Learning and Domain Adaptation--迁移学习 - Array9155/transferlearning

X Description: Everything about Transfer Learning and Domain Adaptation--迁移学习 - Array9155/transferlearning

Opengraph URL: https://github.com/Array9155/transferlearning/tree/master/code

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https://github.com/Array9155/transferlearning
transferlearninghttps://github.com/Array9155/transferlearning/tree/master
Historyhttps://github.com/Array9155/transferlearning/commits/master/code
https://github.com/Array9155/transferlearning/commits/master/code
transferlearninghttps://github.com/Array9155/transferlearning/tree/master
..https://github.com/Array9155/transferlearning/tree/master
BDAhttps://github.com/Array9155/transferlearning/tree/master/code/BDA
BDAhttps://github.com/Array9155/transferlearning/tree/master/code/BDA
deephttps://github.com/Array9155/transferlearning/tree/master/code/deep
deephttps://github.com/Array9155/transferlearning/tree/master/code/deep
distancehttps://github.com/Array9155/transferlearning/tree/master/code/distance
distancehttps://github.com/Array9155/transferlearning/tree/master/code/distance
feature_extractorhttps://github.com/Array9155/transferlearning/tree/master/code/feature_extractor
feature_extractorhttps://github.com/Array9155/transferlearning/tree/master/code/feature_extractor
traditionalhttps://github.com/Array9155/transferlearning/tree/master/code/traditional
traditionalhttps://github.com/Array9155/transferlearning/tree/master/code/traditional
README.mdhttps://github.com/Array9155/transferlearning/blob/master/code/README.md
README.mdhttps://github.com/Array9155/transferlearning/blob/master/code/README.md
README.mdhttps://github.com/Array9155/transferlearning/tree/master/code#readme
https://github.com/Array9155/transferlearning/tree/master/code#code_transfer_learning
herehttps://github.com/jindongwang/transferlearning/blob/master/doc/dataset.md
https://github.com/Array9155/transferlearning/tree/master/code#fine-tune-最简单的深度迁移学习
PyTorchhttps://github.com/jindongwang/transferlearning/tree/master/code/deep/finetune_AlexNet_ResNet
Another example using Pytorchhttps://github.com/Spandan-Madan/Pytorch_fine_tuning_Tutorial
Pytorch CNN fine-tunehttps://github.com/creafz/pytorch-cnn-finetune
Pytorchhttps://github.com/miguelgfierro/sciblog_support/blob/master/A_Gentle_Introduction_to_Transfer_Learning/Intro_Transfer_Learning.ipynb
Tensorflowhttps://cosx.org/2017/10/transfer-learning/
Tensorflowhttps://github.com/tensorflow/hub
https://github.com/Array9155/transferlearning/tree/master/code#deep-feature-extractor-提取深度网络特征用于传统方法
Deep feature extractorhttps://github.com/jindongwang/transferlearning/blob/master/code/feature_extractor/readme.md
https://github.com/Array9155/transferlearning/tree/master/code#basic-distance-常用的距离度量
Matlabhttps://github.com/lopezpaz/classifier_tests/tree/master/code/unit_test_mmd
Pythonhttps://github.com/jindongwang/transferlearning/tree/master/code/distance/mmd.py
Pythonhttps://github.com/jindongwang/transferlearning/tree/master/code/distance/proxy_a_distance.py
Pythonhttps://github.com/jindongwang/transferlearning/tree/master/code/distance/coral_loss.py
Pythonhttps://github.com/metric-learn/metric-learn
scipy.stats.wasserstein_distancehttps://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wasserstein_distance.html
Tensorflowhttps://github.com/google/wasserstein-dist
https://github.com/Array9155/transferlearning/tree/master/code#traditional-transfer-learning-methods--非深度迁移
Matlabhttps://github.com/jindongwang/transferlearning/tree/master/code/traditional/SVM.m
Matlab and Pythonhttps://github.com/jindongwang/transferlearning/tree/master/code/traditional/TCA
Pythonhttps://github.com/jindongwang/transferlearning/tree/master/code/traditional/KMM.py
Matlab and Pythonhttps://github.com/jindongwang/transferlearning/blob/master/code/traditional/GFK
Matlabhttps://github.com/enoonIT/nbnn-nbnl/tree/master/DANBNN_demo
Matlab and Pythonhttps://github.com/jindongwang/transferlearning/blob/master/code/traditional/JDA
Matlabhttps://github.com/jindongwang/transferlearning/blob/master/code/traditional/MyTJM.m
Matlab and Pythonhttps://github.com/jindongwang/transferlearning/blob/master/code/traditional/CORAL
Githubhttps://github.com/VisionLearningGroup/CORAL
Matlab(official)https://www.uow.edu.au/~jz960/codes/JGSA-r.rar
Matlab(easy)https://github.com/jindongwang/transferlearning/blob/master/code/traditional/CORA/MyJGSA.m
Pythonhttps://github.com/chenchiwei/tradaboost
Matlab(official)http://users.cecs.anu.edu.au/~basura/DA_SA/
Matlabhttps://github.com/jindongwang/transferlearning/tree/master/code/traditional/SA_SVM.m
Matlab(official)https://github.com/jindongwang/transferlearning/tree/master/code/BDA
Matlabhttps://github.com/xyh2016/MTLF
Matlab(official)https://github.com/Heliot7/open-set-da
Matlab(official)https://github.com/poppinace/TAISL
Matlabhttps://github.com/jindongwang/activityrecognition/tree/master/code/percom18_stl
Matlabhttp://homes.esat.kuleuven.be/~raljundi/papers/LSA%20Clean%20Code.zip
Matlab(official)http://stevenhoi.org/otl
Matlabhttps://github.com/twanvl/rwa-da/tree/master/src
Matlab(Official)https://github.com/jindongwang/transferlearning/tree/master/code/traditional/MEDA
Matlab(Official)https://github.com/jindongwang/transferlearning/tree/master/code/traditional/EasyTL
Matlabhttps://github.com/amber0309/SCA
https://github.com/Array9155/transferlearning/tree/master/code#deep-transfer-learning-methods--深度迁移
PyTorchhttps://github.com/jindongwang/transferlearning/tree/master/code/deep/DaNN
PyTorchhttps://github.com/jindongwang/transferlearning/tree/master/code/deep/DDC_DeepCoral
PyTorch(recommend)https://github.com/jindongwang/transferlearning/tree/master/code/deep/DeepCoral
PyTorchhttps://github.com/SSARCandy/DeepCORAL
中文解读https://ssarcandy.tw/2017/10/31/deep-coral/
PyTorch(Official)https://github.com/thuml/Xlearn/tree/master/pytorch
Caffe(Official)https://github.com/thuml/Xlearn
PyTorch(DAN)(recommend)https://github.com/jindongwang/transferlearning/tree/master/code/deep/DAN
Caffehttps://github.com/thuml/Xlearn
Tensorflow(Official)https://github.com/erictzeng/adda
Pytorchhttps://github.com/corenel/pytorch-adda
Pytorch(another)https://github.com/jvanvugt/pytorch-domain-adaptation/blob/master/adda.py
Caffe(Official)https://github.com/ddtm/caffe/tree/grl
PyTorchhttps://github.com/jindongwang/transferlearning/tree/master/code/deep/DANN(RevGrad)
Pytorch(another)https://github.com/jvanvugt/pytorch-domain-adaptation/blob/master/revgrad.py
Tensorflow(third party)https://github.com/shucunt/domain_adaptation
Python(official)https://github.com/GRAAL-Research/domain_adversarial_neural_network
Tensorflowhttps://github.com/jindongwang/tf-dann
Tensorflowhttps://github.com/haeusser/learning_by_association
Matlabhttps://github.com/hemanthdv/da-hash
Python(Keras)https://github.com/samotiian/CCSA
Pytorchhttps://github.com/thuml/MTlearn
Caffehttps://github.com/ducksoup/autodial
Pytorchhttps://github.com/fungtion/DSN
Tensorflowhttps://github.com/tensorflow/models/tree/master/research/domain_adaptation
Kerashttps://github.com/ghif/drcn
Pytorchhttps://github.com/fungtion/DRCN
Kerashttps://github.com/ghif/mtae
Matlabhttps://github.com/rahafaljundi/Encoder-Based-Lifelong-learning
Pythonhttps://github.com/pmorerio/minimal-entropy-correlation-alignment
Python(Tensorflow)https://github.com/tolstikhin/wae
Pytorchhttps://github.com/corenel/pytorch-atda#pytorch-atda
Pytorchhttps://github.com/vaibhavnaagar/pixelDA_GAN
Pytorchhttps://github.com/corenel/pytorch-arda
Pytorchhttps://github.com/carpedm20/DiscoGAN-pytorch
Caffe(official)https://github.com/thuml/mada
Pytorch(official)https://github.com/mil-tokyo/MCD_DA
Tensorflowhttps://github.com/ricvolpi/adversarial-feature-augmentation
Tensorflowhttps://github.com/YingZhangDUT/Deep-Mutual-Learning
Pytorchhttps://github.com/Britefury/self-ensemble-visual-domain-adapt
Pytorch(Official)https://github.com/thuml/PADA
Pytorchhttps://github.com/mahfuj9346449/iCAN
Caffehttps://github.com/hellojing89/weightedGANpartialDA
Tensorflowhttps://github.com/Mid-Push/Open_set_domain_adaptation
Pytorchhttps://github.com/jvanvugt/pytorch-domain-adaptation/blob/master/wdgrl.py
Tensorflowhttps://github.com/A-bone1/JDDA
Tensorflowhttps://github.com/rfelixmg/frwgan-eccv18
Tensorflowhttps://github.com/Mid-Push/Moving-Semantic-Transfer-Network
Caffe, Pytorchhttps://github.com/thuml/SAN
Pytorchhttps://github.com/IssamLaradji/M-ADDA
Pytorchhttps://github.com/YU1ut/openset-DA
Tensorflowhttps://github.com/RuiShu/dirt-t
Pytorch(official)https://github.com/thuml/CDAN
Pytorch(third party)https://github.com/thuml/CDAN
Keras(Theano)https://github.com/wzell/cmd
Keras(Theano, journal extension)https://github.com/wzell/mann
Pytorch(Official)https://github.com/ksaito-ut/OPDA_BP
Pytorch(Official)https://github.com/jindongwang/transferlearning/tree/master/code/deep/TCP
Pythonhttps://github.com/lorenmt/mtan
Pytorchhttps://github.com/alinlab/L2T-ww
Pytorchhttps://github.com/VisionLearningGroup/SSDA_MME
Pytorchhttps://github.com/jindongwang/transferlearning/tree/master/code/deep/MRAN
Pytorchhttps://github.com/cmhungsteve/TA3N
Pytorchhttps://github.com/KeiraZhao/MDAN
Tensorflowhttps://github.com/DeepDarkFantasy20/TransferbilityFromAttributionMaps
Pytorchhttps://github.com/AMLab-Amsterdam/DIVA
Tensorflowhttps://github.com/kevinlin311tw/CDCL-human-part-segmentation
PyTorchhttps://github.com/postBG/DTA.pytorch
Pytorchhttps://github.com/jindongwang/transferlearning/tree/master/code/deep/DAAN
https://github.com/Array9155/transferlearning/tree/master/code#applications
Pythonhttps://github.com/sebastianruder/learn-to-select-data
Tensorflowhttps://github.com/timerstime/SDG4DA
Code from HKUSThttp://www.cse.ust.hk/TL/
https://github.com/Array9155/transferlearning/tree/master/code#code-from-hkust-a-bit-old
https://github.com/Array9155/transferlearning/tree/master/code#references
https://doi.org/10.1016/j.ins.2019.01.025https://doi.org/10.1016/j.ins.2019.01.025
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