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TripleE-DG
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## TripleE: Easy Domain Generalization via Episodic Replay By [Xiaomeng Li](https://xmengli.github.io/), [Hongyu Ren](http://hyren.me/), [Huifeng Yao](https://scholar.google.com/citations?hl=en&user=hzNA39EAAAAJ) and [Ziwei Liu](https://liuziwei7.github.io/). This repository is for our paper [TripleE: Easy Domain Generalization via Episodic Replay](https://arxiv.org/pdf/2210.01807.pdf). <!-- <br/> <p align="center"> <img src="figure/framework.png"> </p> --> ## Installation ``` conda env create -f IDC_environment.yml ``` ## Data Preparation * Download [PACS dataset](https://drive.google.com/drive/folders/1SKvzI8bCqW9bcoNLNCrTGbg7gBSw97qO), [Digits-DG dataset](https://drive.google.com/uc?id=15V7EsHfCcfbKgsDmzQKj_DfXt_XYp_P7), [Office-Home dataset](https://drive.google.com/file/d/0B81rNlvomiwed0V1YUxQdC1uOTg/view?resourcekey=0-2SNWq0CDAuWOBRRBL7ZZsw), [VLCS dataset](https://drive.google.com/uc?id=1skwblH1_okBwxWxmRsp9_qi15hyPpxg8), [TerraInc dataset](https://lilablobssc.blob.core.windows.net/caltechcameratraps/eccv_18_all_images_sm.tar.gz), [DomainNet dataset](http://ai.bu.edu/M3SDA/). You can download these datasets using this [script](https://github.com/facebookresearch/DomainBed/blob/main/domainbed/scripts/download.py). * Put data under `./DATASET/` * The correct path should be ``` ./DATASET/PACS/kfold/sketch/... ./DATASET/digits_dg/mnist/... ./DATASET/OfficeHome/Art/... ./DATASET/VLCS/Caltech101... ./DATASET/terra_incognita/L38... ``` ## Training * Check training command in `./scripts/`. * Change `times` to `1,2,3` to run the experiment for 3 times. * Set `target` to get results on different target domains. * By averaging three results on each domain, you will get the following results: | Sketch | Cartoon | Art | Photo | Average | ---------- | :-----------: | :-----------: | :-----------: | :-----------: | 84.45% | 80.84% | 85.30% | 96.27% | 86.72% <!-- * Evaluate our models: download models from [cartoon](https://gohkust-my.sharepoint.com/:u:/g/personal/eexmli_ust_hk/EQUZU9JnCV5Hje30gTN29BkBNDiFYmBQXbDa1F2Gypn59g?e=j56u9G), [art_painting](https://gohkust-my.sharepoint.com/:u:/g/personal/eexmli_ust_hk/EXgBELtc0VFNpgDQnMeufxEB_EkAfTbF5-pvzQrSEfcNbA?e=CnRCKG), [photo](https://gohkust-my.sharepoint.com/:u:/g/personal/eexmli_ust_hk/Ef-B6X98bBNPtH3v2qKLVo0BKE7GlyNSkaWM0T91abOoLg?e=5o8fbG), [sketch](https://gohkust-my.sharepoint.com/:u:/g/personal/eexmli_ust_hk/ETlPaYXHTUdFkc82wkrL5y4BUs7Oom8exsrg-clk0zuyRg?e=ZvvDQU). Specify the path `--resume` for models and run ``` python main_dg.py -a resnet18 --lr 0.001 --epochs 100 --batch-size 4 . --source cartoon sketch photo --target art_painting --ratio 1.0 --result exp/dg/dg_art --baug 4 --gpu 0 --times 1 --ncesole --evaluate --resume exp/model_best_art.pth.tar ``` --> ## Citation If this code is useful for your research, please consider citing: <!-- ```shell @article{li2020self, title={Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis}, author={Li, Xiaomeng and Jia, Mengyu and Islam, Md Tauhidul and Yu, Lequan and Xing, Lei}, journal={IEEE Transactions on Medical Imaging}, year={2020}, publisher={IEEE} } ``` -->
