Paper

Automated Synthetic-to-Real Generalization.
Wuyang Chen, Zhiding Yu, Zhangyang Wang, Anima Anandkumar.
In ICML 2020.

• Visda-17 to COCO

• train resnet101 with only proxy guidance
• train resnet101 with both proxy guidance and L2O policy
• evaluation

• GTA5 to Cityscapes

• Download Visda-17 Dataset

• Download pretrained ResNet101 on Visda17
• Put the checkpoint under ./ASG/pretrained/
• Put the code below in train.sh

python train.py \
--epochs 30 \
--batch-size 32 \
--lr 1e-4 \
--lwf 0.1 \
--resume pretrained/res101_vista17_best.pth.tar \
--evaluate

• Run CUDA_VISIBLE_DEVICES=0 bash train.sh
  • Please update the GPU index via CUDA_VISIBLE_DEVICES based on your need.

• Put the code below in train.sh

python train.py \
--epochs 30 \
--batch-size 32 \
--lr 1e-4 \
--lwf 0.1

• Run CUDA_VISIBLE_DEVICES=0 bash train.sh
  • Please update the GPU index via CUDA_VISIBLE_DEVICES based on your need.

• Download pretrained L2O Policy on Visda17
• Put the checkpoint under ./ASG/pretrained/
• Put the code below in l2o_train.sh

python train.py \
--epochs 30 \
--batch-size 32 \
--lr 1e-4 \
--lwf 0.1

• Run CUDA_VISIBLE_DEVICES=0 bash l2o_train.sh
  • Please update the GPU index via CUDA_VISIBLE_DEVICES based on your need.

If you use this code for your research, please cite:

@incollection{chen2020automated,
 author = {Chen, Wuyang and Yu, Zhiding and Wang, Zhangyang and Anandkumar, Anima},
 booktitle = {Proceedings of Machine Learning and Systems 2020},
 pages = {8272--8282},
 title = {Automated Synthetic-to-Real Generalization},
 year = {2020}
}