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.

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

Abstract

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.

Results

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

Deep CORAL	DDC

CDAN	CDAN+E

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

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 main.py --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 plot_loss_acc.py --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

Requirements

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

References

• DeepCORAL paper
• DDC paper
• CDAN paper