Active Continuous Continual Domain Adaptation

This repo combines the multi-anchor active domain adaptation method with continous, continual domain adaptation method into a novel method: ACCDA. With the help of active sampling strategy, ACCDA helps generalizes neural networks trained on source dataset to unseen target datasets with continously changing visual conditions. For more information (e.g., paper, API and detailed documentation), please kindly visit our project website.

Data

The data directory (only containing file paths) is meant to store the original versions of the GTA5, VIPER, SYNTHIA and CITYSCAPES datasets. These datasets are available online, and are not included in this repository.

Running the code

The datasets need to be prepared before running the E2E model. This is done via the process_gta5_cityscapes.ipynb or process_synthia_cityscapes.ipynb notebooks. The processed images will be available in the processed-data folder.

After processing the dataset, E2E training and adaptation can be done by running vgg16-deeplabv3-GTA5-CITYSCAPES.ipynb, vgg16-deeplabv3-SYNTHIA-CITYSCAPES.ipynb, and ACCDA.ipynb.

The notebooks will save model weights in the weights folder. Currently, this folder comes prepopulated with weights corresponding to the runs present in the notebooks.

The notebooks should be self-explanatory, but more information and documentation can be found on our project website.

Original Codebases

The entire codebase is adapted from "Unsupervised Model Adaptation for Continual Semantic Segmentation" The code was tested using Tensorflow 2.2 and CUDA 11.2, with driver version 460.xx.

@misc{stan2021unsupervised,
      title={Unsupervised Model Adaptation for Continual Semantic Segmentation}, 
      author={Serban Stan and Mohammad Rostami},
      year={2021},
      eprint={2009.12518},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

The MADA method for active domain adaptation is adapted from the PyTorch codebase.

@inproceedings{ning2021multi,
  title={Multi-Anchor Active Domain Adaptation for Semantic Segmentation},
  author={Ning, Munan and Lu, Donghuan and Wei, Dong and Bian, Cheng and Yuan, Chenglang and Yu, Shuang and Ma, Kai and Zheng, Yefeng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9112--9122},
  year={2021}
}