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Disentangling Masked Autoencoders for Unsupervised Domain Generalization

This is the source code accompanying the paper Disentangling Masked Autoencoders for Unsupervised Domain Generalization by An Zhang*, Han Wang*, Xiang Wang, Tat-Seng Chua
(* denotes equal contribution)

flow

Dataset Preparation

DomainNet

Please download DomainNet and place the data in the ./dataset
Run the following command to split the dataset for the unsupervised domain generalization setting
```
cd ./dataset
./split.sh
```

Training

See the demos in ./script/.

Pretraining

Please run the following command to pretrain the model on the painting, real, and sketch domains:

python ../train.py \
    --dataset DomainNet \
    --test_envs 0 1 3 \
    --batch_size 96 \
    --mask_ratio 0.80 \
    --lr 1e-4 \
    --epochs 501 \
    --lambda1 1e-3 \
    --lambda2 0.0 \
    --is_linear \
    --augment

Replace --test_envs 0 1 3 with --test_envs 2 4 5 to pretrain on the clipart, infograph, quickdraw domains.

Finetuning

Please run the following command to finetune pretrained model with label fraction 100%:

python ../train_finetune.py \
    --test_envs 0 1 3 \
    --dataset DomainNet \
    --batch_size 12 \
    --lr 5e-5 \
    --val_fraction 1.00 \
    --holdout_fraction 0.10 \
    --epochs 51 \
    --resume /path/to/checkpoints \
    --augment

Modify val_fraction and holdout_fraction to set different label fractions. Detailed information can be found in train_finetune.py function get_args_parser().

Citation

If you find our code useful, please consider citing:

@article{zhang2024disentangling,
  title={Disentangling Masked Autoencoders for Unsupervised Domain Generalization},
  author={Zhang, An and Wang, Han and Wang, Xiang and Chua, Tat-Seng},
  journal={arXiv preprint arXiv:2407.07544},
  year={2024}
}