DA-MM

This is the transfer learning library for the following paper: Deep transfer learning method based on automatic Domain Alignment and Moment Matching

Deep Transfer Learning on Caffe

This is a caffe library for deep transfer learning. We fork the repository with version ID 29cdee7 from Caffe, Xlearn, Autodial, B-JMMD and make our modifications. The main modifications is listed as follow:

Add mmd layer described in paper "Learning Transferable Features with Deep Adaptation Networks" (ICML '15).
Add jmmd layer` described in paper "Deep Transfer Learning with Joint Adaptation Networks" (ICML '17).
Add entropy layer and outerproduct layer described in paper "Unsupervised Domain Adaptation with Residual Transfer Networks" (NIPS '16).
Add DialLayer: implements the AutoDIAL layer described in paper "AutoDIAL: Automatic DomaIn Alignment Layers" (ICCV '17).
Add EntropyLossLayer: a simple entropy loss implementation with integrated softmax computation described in paper "AutoDIAL: Automatic DomaIn Alignment Layers" (ICCV '17)..
Add bjmmd layer described in paper "Balanced joint maximum mean discrepancy for deep transfer learning" (AA '2020).

Data Preparation

In data/office/*.txt, we give the lists of three domains in Office dataset.

We have published the Image-Clef dataset we use here.

Training Model

In \models\autodial, we give an example model based on different networks to show how to transfer from amazon to webcam. The bvlc_reference_caffenet is used as the pre-trained model for Alexnet. The deep-residual-networks is used as the pre-trained model for Resnet. We use Resnet-50. The bvlc_googlenet.caffemodel is used as the pre-trained model for Inception. If the Office dataset and pre-trained caffemodel are prepared, the example can be run with the following command:

Auto+MMD： Examples of different network implementations For Alexnet:

"TOOLS=./build/tools
LOG=models/autodial/alexnet/mmd-auto/office-caltech1/AC/logs-auto-AC-0.1-626-64.log
$TOOLS/caffe train \
--solver=models/autodial/alexnet/mmd-auto/office-caltech1/AC/solver.prototxt -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel -gpu all 2>&1 | tee $LOG"

For Inception:

"TOOLS=./build/tools
LOG=models/autodial/inception/mmd-auto/office31/logs-aw-autoMMD-google-test-60000-703-1.0-0.2.log
$TOOLS/caffe train \
--solver=models/autodial/inception/mmd-auto/office31/solver.prototxt -weights models/bvlc_googlenet/bvlc_googlenet.caffemodel -gpu all 2>&1 | tee $LOG"

The commands of Auto+JMMD and Auto+BJMMD are similar to those of Auto+MMD

Changing Transfer Task

If you want to change to other transfer tasks (e.g. webcam to amazon), you may need to:

In train_val.prototxt please change the source and target datasets;
In solver.prototxt please change test_iter to the size of the target dataset: 2817 for amazon, 795 for webcam and 498 for dslr;

Citation

If you use this code for your research, please consider citing:

@Article{math10142531,
AUTHOR = {Zhang, Jingui and Meng, Chuangji and Xu, Cunlu and Ma, Jingyong and Su, Wei},
TITLE = {Deep Transfer Learning Method Based on Automatic Domain Alignment and Moment Matching},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {14},
ARTICLE-NUMBER = {2531},
URL = {https://www.mdpi.com/2227-7390/10/14/2531},
ISSN = {2227-7390},
DOI = {10.3390/math10142531}
}

       
@article{meng2021balanced,
  title={Balanced joint maximum mean discrepancy for deep transfer learning},
  author={Meng, Chuangji and Xu, Cunlu and Lei, Qin and Su, Wei and Wu, Jinzhao},
  journal={Analysis and Applications},
  volume={19},
  number={03},
  pages={491--508},
  year={2021},
  publisher={World Scientific}
}

Contact

If you have any problem about our code, feel free to contact

or describe your problem in Issues.

mengchuangji / DA-MM