denograd 0.1.1

Reduces the noise level of the input data of a Neural Network

DenoGrad: Deep Gradient Denoising Framework

📄 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

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

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} }