Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships

Official implementation of "Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships", NeurIPS 2023.

Environment Setup

This project is implemented using PyTorch and PyTorch Geometric as the backbone. A conda environment with all related dependencies can be created as follows:

1. Clone the project repository:

git clone https://github.com/abhrac/trd.git
cd trd

2. Install dependencies and activate conda environment:

conda env create -f environment.yml
conda activate trd

3. Run:

python3 src/main.py --dataset='FGVCAircraft' --seed=0 --model-type=multiview_hausdorff --train-backbone --crop-mode=random --local-weight=1e-4 --train-bsize=8 --gpu=1 --recovery-epoch=1

The run_expt.sh file contains sample training commands.

Citation

@inproceedings{
  chaudhuri2023TRD,
  title={Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships},
  author={Abhra Chaudhuri and Massimiliano Mancini and Zeynep Akata and Anjan Dutta},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=wUNPmdE273}
}