Awesome
Multitask Learning Strengthens Adversarial Robustness
@inproceedings{mao2020multitask,
author = {Chengzhi Mao and
Amogh Gupta and
Vikram Nitin and
Baishakhi Ray and
Shuran Song and
Junfeng Yang and
Carl Vondrick},
title = {Multitask Learning Strengthens Adversarial Robustness},
booktitle = {Computer Vision - {ECCV} 2020 - 16th European Conference, Glasgow,
UK, August 23-28, 2020, Proceedings, Part {II}},
series = {Lecture Notes in Computer Science},
volume = {12347},
pages = {158--174},
publisher = {Springer},
year = {2020},
url = {https://doi.org/10.1007/978-3-030-58536-5\_10},
doi = {10.1007/978-3-030-58536-5\_10},
}
Demo for Robustness under multitask attack
Download Cityscapes dataset from Cityscapes.
Download pretrained DRN-22 model from DRN model zoo.
Modify the path to data and model in demo_mtlrobust.py
.
Run demo to see the trend that model overall robustness is increased when the output dimension increased.
To see the gradient norm measurement of robustness, set get_grad=True
,
To see the actually robust accuracy for model, set test_acc_output_dim=False
python demo_mtlrobust.py
which explains why segmentation is inherently robust.
CityScape
Data preprocessing
Run python data_resize_cityscape.py
to resize to smaller images.
Train Robust model against single task attack
-
Set up the path to data in
config/drn_d_22_cityscape_config.json
-
Run
cityscape_example.sh
to train a main task with auxiliary task for robustness.
Taskonomy
Data Preprocessing
You can use our preprocessed data from preprocessed data
Or do from scratch
-
Download data from official raw data.
-
Run
python data_resize_taskonomy.py
to resize to smaller images. -
Rename
segment_semantic
tosegmentsemantic
.
Train Robust model against single task attack
-
Set up the path to data in
config/resnet18_taskonomy_config.json
-
Run
taskonomy_example.sh
to train a main task with auxiliary task for robustness. For different task, we have different different setup, refer to our paper and supplementary for details.
Model evaluation
We offer our pretrained models to download here: Cityscapes segmentation depth and Taskonomy taskonomy segmentation demo
After setting up the path to your downloaded models in test_cityscapes_seg.py
and test_taskonomy_seg.py
,
Run python test_cityscapes_seg.py
and python test_taskonomy_seg.py
for evaluating the robustness of multitask models under
single task attacks.
Pretrained models for other tasks for Taskonomy can be downloaded [here, comming soon](comming soon)
Acknowledgement
Our code refer the code at: https://github.com/fyu/drn/blob/master/drn.py Taskonomy https://github.com/tstandley/taskgrouping,
We thank the authors for open sourcing their code.