A Batch Normalization Classifier for Domain Adaptation Matthew R. Behrend and Sean M. Robinson
Manually download the OfficeHome dataset from https://drive.google.com/file/d/0B81rNlvomiwed0V1YUxQdC1uOTg/view Extract the zip file and move contents to ./data/officehome
Initial feature extraction may take some time. The result will be stored in a numpy archive in ./data_cache for fast loading
developed with python 3.7.9
pip3 install --upgrade pip
pip3 install -r requirements.txtTo reproduce data from paper
python run_experiments.py/adaptation_methods/adapt_BNC.py contains the model and training methods
class BNC implements the batch normalized classifier and training loop class BNC_Cotrained uses cotraining on source and target domains for comparison to the source-free adaptation in class BNC
class BNCInspect explores feature distributions in an ablation study of the batchnorm layer
Please cite our paper if you use this code
M.R. Behrend and S.M. Robinson, "A Batch Normalization Classifier for Domain Adaptation", arXiv e-prints, p. arXiv:2103.11642, 2021.
https://arxiv.org/abs/2103.11642
Matthew Behrend behrend04@gmail.com
Sean Robinson sean@psl.com
MIT License
Dataset loading uses portions of code from the following sources: Tzeng2017, Ringwald 2020 Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition. pages 7167-7176, 2017. Tobias Ringwald and Rainer Stiefelhagen. Unsupervised Domain Adaptation by Uncertain Feature Alignment. arXiv preprint arXiv:2009.06483, 2020.