Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing

OUTPUT_DIR: Directory to save output contents.
DATA_DIR: Directory containing the datasets.
MODEL_DIR: Directory containing the trained models.

Environment:

conda_env_local.yml file can be used to create an anaconda environment to run the code.

Training script:

To train the One-De model on cityscapes dataset:

python train.py --batch-size 8 --workers 8 --data-folder /DATA_DIR/Cityscapes --crop-size 512 1024 --checkname train_cs --config-file ./model_cfgs/cityscapes/one_de.yaml --epochs 140 --lr .0001 --output-dir OUTPUT_DIR --lr-strategy stepwise --lr-decay 98 126 --base-optimizer RAdam --dataset cityscapes

Other model configs can be found in 'model_cfgs' directory.

Eval models:

Models can be evaluated using --eval-only arg along with train script.

Get CKA similarities and task groupings:

The following code runs grouping using seperate decoder (Sep-De).

python explain.py --batch-size 4 --workers 0 --crop-size 480 640 --config-file ./model_cfgs/cityscapes/sep_de_group.yaml --resume MODEL_DIR/model_latest_140.pth --data-folder /DATA_DIR/NYUv2 --data-folder-1 /DATA_DIR/NYUv2/image/train --explainer-name CKA --compare-tasks --dataset cityscapes

Cite Our Work

If you find the code concerning Progressive Decoder Fusion (PDF) useful in your research, please consider citing our paper:

@InProceedings{pmlr-v199-gurulingan22a,
  title = 	 {Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing},
  author =       {Gurulingan, Naresh Kumar and Arani, Elahe and Zonooz, Bahram},
  booktitle = 	 {Proceedings of The 1st Conference on Lifelong Learning Agents},
  pages = 	 {937--951},
  year = 	 {2022},
  editor = 	 {Chandar, Sarath and Pascanu, Razvan and Precup, Doina},
  volume = 	 {199},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {22--24 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v199/gurulingan22a/gurulingan22a.pdf},
  url = 	 {https://proceedings.mlr.press/v199/gurulingan22a.html},
}