README.md

---
license: mit
datasets:
- uoft-cs/cifar10
- uoft-cs/cifar100
- ILSVRC/imagenet-1k
tags:
- Adversarial Robustness
---

# MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers

This is the official **model** repository of the preprint paper \
*[MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers](https://arxiv.org/abs/2402.02263)* \
by [Yatong Bai](https://bai-yt.github.io), [Mo Zhou](https://cdluminate.github.io), [Vishal M. Patel](https://engineering.jhu.edu/faculty/vishal-patel),
and [Somayeh Sojoudi](https://www2.eecs.berkeley.edu/Faculty/Homepages/sojoudi.html) in Transactions on Machine Learning Research.

<center>
  <img src="main_figure.png" alt="MixedNUTS Results" title="Results" width="800"/>
</center>

**TL;DR:** MixedNUTS balances clean data classification accuracy and adversarial robustness without additional training
via a mixed classifier with nonlinear base model logit transformations.

## Model Checkpoints

MixedNUTS is a training-free method that has no additional neural network components other than its base classifiers.

All robust base classifiers used in the main results of our paper are available on [RobustBench](https://robustbench.github.io)
and can be downloaded automatically via the RobustBench API.

Here, we provide the download links to the standard base classifiers used in the main results.

| Dataset   | Link  |
|-----------|-------|
| CIFAR-10  | [Download](https://huggingface.co/Bai-YT/MixedNUTS/resolve/main/cifar10_std_rn152.pt?download=true)  |
| CIFAR-100 | [Download](https://huggingface.co/Bai-YT/MixedNUTS/resolve/main/cifar100_std_rn152.pt?download=true) |
| ImageNet  | [Download](https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt) |

**For code and detailed usage, please refer to our [GitHub repository](https://github.com/Bai-YT/MixedNUTS).**


## Citing our work (BibTeX)

```bibtex
@article{MixedNUTS,
  title={MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers},
  author={Bai, Yatong and Zhou, Mo and Patel, Vishal M. and Sojoudi, Somayeh},
  journal={Transactions on Machine Learning Research},
  year={2024}
}
```