Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation

Code for the paper:

Mert Keser*, Artem Savkin*, Federico Tombari, "Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation", IEEE IROS 2021

Requirements

Dockerfile:

FROM nvidia/cuda:9.2-cudnn7-runtime-ubuntu18.04
RUN apt-get update
RUN apt-get update && apt-get install -y python3-dev python3-pip
RUN pip3 install --upgrade pip
RUN pip3 install torch==1.3.1
RUN pip3 install torchvision==0.4.2

Usage

Train command:

python secogan/train.py \
    --name=<experiment_name> \
    --gpu_ids=0 \
    --data_source=<source_data_path> \
    --data_target=<target_data_path> \
    --output_dir=<output_path> \
    --batch_size=4

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{Keser2021,
    title={Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation},
    author={Keser, Mert and Savkin, Artem and Tombari, Federico},
    booktitle={IEEE IROS},
    year={2021}
}

Acknowledgments

This repo heavily borrows code from MUNIT, SAE.