# DenoGrad: Deep Gradient Denoising Framework

[![arXiv](https://img.shields.io/badge/arXiv-2511.10161-b31b1b.svg)](https://arxiv.org/abs/2511.10161)
[![Python](https://img.shields.io/badge/Python-3.8%2B-blue)](https://www.python.org/)
[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)

## 📄 Description

**DenoGrad** is a novel gradient-based denoising framework designed to enhance the robustness and performance of Artificial Intelligence models, with a specific focus on interpretable (white-box) models.

Unlike conventional techniques that simply remove noisy instances or significantly alter the data distribution, DenoGrad leverages the gradients of a reference Deep Learning (DL) model—trained on the target data—to dynamically detect and correct noisy samples.

### 🚀 Key Features

* **Gradient-Based Correction:** Utilizes gradient information from deep models to guide the noise reduction process effectively.
* **Distribution Preservation:** Corrects instances while maintaining the original data distribution, avoiding oversimplification of the problem space.
* **Task Agnostic:** Validated effectively on both tabular data and time-series datasets.
* **Interpretable AI Enhancement:** Specifically engineered to boost the performance of interpretable models in noisy environments without sacrificing transparency.

## 🛠️ Installation

```bash
git clone [https://github.com/your-username/DenoGrad.git](https://github.com/your-username/DenoGrad.git)
cd DenoGrad
pip install -r requirements.txt
```

## 📖 Basic Usage

```python
from denograd import DenoGrad

# Example usage (adapt to your actual API)
# Initialize the denoiser with your reference model
denoiser = DenoGrad(model=my_deep_model)

# Denoise the dataset
clean_data = denoiser.denoise(noisy_data)
```

## 📝 Citation

If you use DenoGrad in your research, please cite our paper:


> @article{denograd2025,
  title={DenoGrad: Deep Gradient Denoising Framework for Enhancing the Performance of Interpretable AI Models},
  author={Alonso-Ramos, J. Javier and [Other Authors]},
  journal={arXiv preprint arXiv:2511.10161},
  year={2025}
}
