Introduction

TransPar is an open-source and well-documented library for Transferable Parameter Learning. It is based on pure PyTorch and the Transfer Learning Library (Nice library!). Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.

Currently, we intergrate TransPar with below nice algorithms:

Domain Adaptation for Classification

• Domain-Adversarial Training of Neural Networks (DANN, ICML 2015)
• Learning Transferable Features with Deep Adaptation Networks (DAN, ICML 2015)
• Deep Transfer Learning with Joint Adaptation Networks (JAN, ICML 2017)
• Conditional Adversarial Domain Adaptation (CDAN, NIPS 2018)
• Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (MCD, CVPR 2018)
• Larger Norm More Transferable: An Adaptive Feature Norm Approach for
• Bridging Theory and Algorithm for Domain Adaptation (MDD, ICML 2019)

Domain Adaptation for Keypoint Detection

• Regressive Domain Adaptation for Unsupervised Keypoint Detection (RegDA, CVPR 2021)

Installation

For flexible use and modification, please git clone the library.

Documentation

You can find the tutorial and API documentation on the website: Documentation (please open in Firefox or Safari). Note that this link is only for temporary use. You can also build the doc by yourself following the instructions in http://170.106.108.162/get_started/faq.html.

Also, we have examples in the directory examples. A typical usage is

# Train a DANN on Office-31 Amazon -> Webcam task using ResNet 50.
# Assume you have put the datasets under the path `data/office-31`, 
# or you are glad to download the datasets automatically from the Internet to this path
python dann-TransPar.py data/office31 -d Office31 -s A -t W -a resnet50  --epochs 20

In the directory examples, you can find all the necessary running scripts to reproduce the benchmarks with specified hyper-parameters.

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have licenses to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

Contact

If you have any problem with our code or have some suggestions, including the future feature, feel free to contact

• Zhongyi Han (hanzhongyicn@gmail.com)

or describe it in Issues.

Citation

If you use this toolbox or benchmark in your research, please cite this project.