Deep Unsupervised Domain Adaptation

Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31.
Alternatives To Deep Unsupervised Domain Adaptation
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Fashion Mnist9,856
a year ago24mitPython
A MNIST-like fashion product database. Benchmark :point_down:
Awesome Semantic Segmentation8,065
2 years ago13
:metal: awesome-semantic-segmentation
4 months ago19mit
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
4 months ago71Python
中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard
4 months ago69otherPython
Github of the FaceForensics dataset
Benchmarking Gnns1,913
7 months ago2mitJupyter Notebook
Repository for benchmarking graph neural networks
a year ago7mitJupyter Notebook
Datasets, tools, and benchmarks for representation learning of code.
a year ago9mitPython
State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.
15 days ago14mitC#
Beir80833 days ago28June 30, 202253apache-2.0Python
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Alternatives To Deep Unsupervised Domain Adaptation
Select To Compare

Alternative Project Comparisons


Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31.

Paper: Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition


It has been well proved that deep networks are efficient at extracting features from a given (source) labeled dataset. However, it is not always the case that they can generalize well to other (target) datasets which very often have a different underlying distribution. In this report, we evaluate four different domain adaptation techniques for image classification tasks: Deep CORAL, Deep Domain Confusion (DDC), Conditional Adversarial Domain Adaptation (CDAN) and CDAN with Entropy Conditioning (CDAN+E). The selected domain adaptation techniques are unsupervised techniques where the target dataset will not carry any labels during training phase. The experiments are conducted on the office-31 dataset.


Accuracy performance on the Office31 dataset for the source and domain data distributions (with and without transfer losses).


Target accuracies for all six domain shifts in Office31 dataset (amazon, webcam and dslr)

Method A → W A → D W → A W → D D → A D → W
No Adaptaion 43.1 ± 2.5 49.2 ± 3.7 35.6 ± 0.6 94.2 ± 3.1 35.4 ± 0.7 90.9 ± 2.4
DeepCORAL 49.5 ± 2.7 40.0 ± 3.3 38.3 ± 0.4 74.4 ± 4.3 38.5 ± 1.5 89.1 ± 4.4
DDC 41.7 ± 9.1 --- --- --- --- ---
CDAN 44.9 ± 3.3 49.5 ± 4.6 34.8 ± 2.4 93.3 ± 3.4 32.9 ± 3.4 88.3 ± 3.8
CDAN+E 48.7 ± 7.5 53.7 ± 4.7 35.3 ± 2.7 93.6 ± 3.4 33.9 ± 2.2 87.7 ± 4.0

Training and inference

To train the model in your computer you must download the Office31 dataset and put it in your data folder.

Execute training of a method by going to its folder (e.g. DeepCORAL):

cd DeepCORAL/
python --epochs 100 --batch_size_source 128 --batch_size_target 128 --name_source amazon --name_target webcam

Loss and accuracy plots

Once the model is trained, you can generate plots like the ones shown above by running:

cd DeepCORAL/
python --source amazon --target webcam --no_epochs 10

The following is a list of the arguments the usuer can provide:

  • --epochs number of training epochs
  • --batch_size_source batch size of source data
  • --batch_size_target batch size of target data
  • --name_source name of source dataset
  • --name_target name of source dataset
  • --num_classes no. classes in dataset
  • --load_model flag to load pretrained model (AlexNet by default)
  • --adapt_domain bool argument to train with or without specific transfer loss


  • tqdm
  • PyTorch
  • matplotlib
  • numpy
  • pickle
  • scikit-image
  • torchvision


Popular Dataset Projects
Popular Benchmark Projects
Popular Data Processing Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Neural Network
Convolutional Neural Networks
Unsupervised Learning
Domain Adaptation