This folder contains the codes for the book Introduction to Transfer Learning: Algorithms and Practice. 迁移学习导论.
Links for the Chinese book (2nd edition) can be found at: links.md. 中文第二版书中的链接请见这里。
- For algorithm chapters (chapters 1 ~ 11), we mainly use Office-31 dataset, download HERE:
- For non-deep learning methods (chapters 1~7), we use ResNet-50 pre-trained features. Thus, download the ResNet-50 features.
- For deep learning methods (chapters 8~11), we use Office-31 original dataset. Thus, download the raw images.
- For application chapters (chapters 15~19), the datasets download link can be found at respective chapters.
The following is a basic environment to run most experiments. No special tricky packages are needed. Just pip install -r requirements.txt.
- Python 3.x
- scikit-learn
- numpy
- scipy
- torch
- torchvision
If you find the code or the book helpful, please consider citing our book as:
@book{tlbook,
author = {Wang, Jindong and Chen, Yiqiang},
title = {Introduction to Transfer Learning: Algorithms and Practice},
year = {2023},
url = {jd92.wang/tlbook},
publisher = {Springer Nature}
}
@book{tlbookchinese,
author = {王晋东 and 陈益强},
title = {迁移学习导论},
year = {2021},
url = {jd92.wang/tlbook}
}
My unified transfer learning repo (and the most popular transfer learning repo on Github) has everything you need for transfer learning: https://github.com/jindongwang/transferlearning. Including: Papers, codes, datasets, benchmarks, applications etc.
