/discriminative-classification-fDiv

[ICML2024] Official Pytorch implementation of "f-Divergence Based Classification: Beyond the Use of Cross-Entropy"

Primary LanguagePythonMIT LicenseMIT

$f$-Divergence Based Classification: Beyond the Use of Cross-Entropy

Nicola Novello and Andrea M. Tonello

Official repository of the paper " $f$-Divergence Based Classification: Beyond the Use of Cross-Entropy " published at ICML 2024.

How to run the code

For the image classification tasks, the file main.py runs the experiments. The code runs iterating over multiple random seeds, network architectures and objective functions. They can be set by modifying the lists:

list_cost_func_v = [5] 
random_seeds = [0]
net_architectures = ["ResNet18"] 
dataset_type = "cifar10"

where the IDs of the objective functions are:

  • 2: GAN
  • 3: CE
  • 5: SL
  • 7: KL with softplus as last activation function
  • 9: RKL
  • 10: HD
  • 12: P

while the available network architectures are:

  • ResNet18
  • PreActResNet18
  • MobileNetV2
  • VGG
  • SimpleDLA
  • DenseNet121

For the decoding tasks, the file main_communications.py runs the experiments.

References and Acknowledgments

If you use your code for your research, please cite our paper:

@article{novello2024f,
  title={$ f $-Divergence Based Classification: Beyond the Use of Cross-Entropy},
  author={Novello, Nicola and Tonello, Andrea M},
  journal={arXiv preprint arXiv:2401.01268},
  year={2024}
}

The implementation is based on / inspired by:

Contact

nicola.novello@aau.at