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MemSAC (ECCV 2022)

We present MemSAC (Memory Augmented Sample Consistency for Large-Scale Domain Adaptation) for unsupervised domain adaptation across datasets with many categories or fine-grained classes.

MemSAC_Teaser

The following dependencies are required.

Ubuntu 18.04
Python==3.7.4
numpy==1.19.2
PyTorch==1.4.0, torchvision==0.6.0, cudatoolkit==10.1

Data

The datasets can be downloaded using the following links.

DomainNet: http://ai.bu.edu/M3SDA/.
CUB-Paintings: https://drive.google.com/file/d/1G327KsD93eyGTjMmByuVy9sk4tlEOyK3/view?usp=sharing (from https://github.com/thuml/PAN).

Download the datasets into folder called data/.

Training model on DomainNet

To train the model on DomainNet, run the following script.

bash jobs/domainNet_345.sh <source> <target> <Path for DomainNet dataset> <queue_size>

For example,

bash jobs/domainNet_345.sh real clipart ./data/DomainNet/ 48000

To train the model on CUB-Drawings, run the following script.

bash jobs/cub200.sh <source> <target> <Path for cub2011 dataset> <queue_size>

Testing using trained model.

To directly test our trained model, download the models available at the following links.

Method	Trained Model
DomainNet	Link
CUB-200	Link

CUB-200 dataset

python eval.py --nClasses 200 --checkpoint drawing_cub.pth.tar --data_dir <Path for cub2011 dataset> --batch_size 64 --dataset cub2011 --target cub

DomainNet

python eval.py --nClasses 345 --checkpoint real_clipart.pth.tar --data_dir <Path for domainNet dataset>  --dataset domainNet --target clipart

If you find MemSAC useful for your work please cite:

@article{kalluri2022memsac
  author    = {Kalluri, Tarun and Sharma, Astuti and Chandraker, Manmohan},
  title     = {MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation},
  journal   = {ECCV},
  year      = {2022},
}