This is the pytorch implementation of the paper "Contrastive Supervised Learning on Domain Generalization and Application to Data Corruption".

• A Python install version 3.7
• A PyTorch and torchvision installation version 1.7.0 and 0.8.1, respectively.

• The caffe model we used for AlexNet. Once downloaded, move it into ./alexnet_caffe.pth.tar
• PACS dataset
• OfficeHome dataset
• CIFAR-10-C

You can train the model from scratch :

• cd MDG
• python main.py --data_dir ./data_dir --model AlexNet --datasets PACS
  • data_dir: the dataset directory
  • model: AlexNet or ResNet18
  • datasets: PACS or OfficeHome

You can train the model from scratch :

• cd SDG
• python main.py --data_dir ./cifar10_dir --target_dir ./cifar10C_dir --file_name acc.csv
  • data_dir: the dataset directory for cifar10
  • target_dir: the dataset directory for cifar10-C
  • file_name: the name of file storing the test accuracy