Merge pull request #10 from AdaptiveMotorControlLab/MMathisLab/update-readme

Update README.md

Author: hsirm

README.md

@@ -5,25 +5,34 @@
 This repository contains the code for the paper **"Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings"**. The method, termed **AROS**, employs Neural Ordinary Differential Equations (NODEs) with Lyapunov stability to create robust embeddings for OOD detection, significantly improving performance against adversarial attacks.
 This repository contains the code for the paper **"Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings"**. The method, termed **AROS**, employs Neural Ordinary Differential Equations (NODEs) with Lyapunov stability to create robust embeddings for OOD detection, significantly improving performance against adversarial attacks. Additionally, the repository includes two notebooks: one demonstrates the training and evaluation process on the CIFAR-10 and CIFAR-100 datasets, while the other focuses on the ablation study.
 
+![AROS](https://github.com/user-attachments/assets/b0d9e7f8-e39d-4bae-aee2-79a247b5e87f)
+
 
 ## Key Features
 
 - **Lyapunov Stability for OOD Detection**: Ensures that perturbed inputs converge back to stable equilibrium points, improving robustness against adversarial attacks.
 - **Fake Embedding Crafting Strategy**: Generates fake OOD embeddings by sampling from the low-likelihood regions of the ID data feature space, eliminating the need for additional OOD datasets.
 - **Orthogonal Binary Layer**: Enhances separation between ID and OOD embeddings, further improving robustness.
 
+## Demo 
+
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AdaptiveMotorControlLab/AROS/blob/main/Notebooks/AROS.ipynb) This notebook is designed to replicate and analyze the results presented in Table 1 of the AROS paper, focusing on out-of-distribution detection performance under both attack scenarios and clean evaluation.
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/AdaptiveMotorControlLab/AROS/blob/main/Notebooks/Ablation_Study.ipynb) This notebook is designed to demo the ablation study.
+
+
 ## Repository Structure
 
 
-- **AROS/**
+- **`AROS/`**
   - **`data_loader.py`**: Contains the data loading utilities for training and evaluation.
   - **`evaluate.py`**: Implements the evaluation metrics and testing routines for the AROS model.
   - **`Main.py`**: The main script for training and testing AROS, combining all components.
   - **`stability_loss_function.py`**: Defines the Lyapunov-based loss function used for stabilizing the NODE dynamics.
   - **`utils.py`**: Includes various helper functions used throughout the project.
 - **`requirements.txt`**: Lists the dependencies required to run the project.
-
-
+- **`Notebooks/`**
+  - **`AROS.ipynb`**
+  - **`Notebooks/Ablation_Study.ipynb`**
 
 ## Installation