Awesome

Conditional Risk Minimization(CRM) for making better mistakes

Simplified Code for our Paper No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks, ICLR 2021 Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi

Paper: https://openreview.net/pdf?id=193sEnKY1ij

We resort to the classical Conditional Risk Minimization (CRM) framework for hierarchy aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra parameters; it requires adding just one line of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top-k predictions across datasets, with very little loss in accuracy. Since CRM does not require retraining or fine-tuning of any hyperparameter, it can be used with any off-the-shelf cross-entropy trained model.

Installation

Follow the setup presented in https://github.com/fiveai/making-better-mistakes to prepare the dataset splits as well the conda environment.

Training and Pre-Trained Model

Train the models using the code available in https://github.com/fiveai/making-better-mistakes. The pre-trained models can be found here: tiered-ImageNet: https://drive.google.com/file/d/1gMcpBSKTuN_E5dPQm2ZFF_WUSOOUI9yO/view?usp=sharing

iNaturalist-19: https://drive.google.com/file/d/1gZX10ac03ZSFL9-3K_VmND6SnaZobdqw/view?usp=sharing Alternatively, this codebase should work with most feedforward models in implemented in PyTorch with just a few modifications in compute_results.py

Usage

Set the paths to the tiered-ImageNet and/or iNaturalist19 dataset in data_paths.yml
Given a folder with checkpoints, python3 src/optimal_epoch.py <path_to_directory> can be used to find the best 5 checkpoints to benchmark.
Set the paths to the checkpoint in the corresponding experiment file in experiments/<experiment.sh>
bash experiments/experiment.sh should reproduce the numbers present in the corresponding log file logs/experiment.csv

Each row in the log file corresponds to the results of one checkpoint. The format of each row is: top-1 accuracy, hierarchical distance@1, hierarchical distance@5, hierarchical distance@20, avg. mistake severity, ECE, MCE

If you discover any bugs in the code please contact me, I will cross-check them with my nightmares.

Citation

If you find our work useful/interesting, please consider citing:

@inproceedings{
karthik2021no,
title={No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks},
author={Shyamgopal Karthik and Ameya Prabhu and Puneet K. Dokania and Vineet Gandhi},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=193sEnKY1ij}
}

If you find the dataset splits as well as the class hierarchies useful, please cite:

@InProceedings{bertinetto2020making,
author = {Bertinetto, Luca and Mueller, Romain and Tertikas, Konstantinos and Samangooei, Sina and Lord, Nicholas A.},
title = {Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

License

MIT