Non-Deterministic Variational Autoencoder (VAE) for Data Generation

A comprehensive implementation of a Variational Autoencoder (VAE) for unsupervised data generation with uncertainty quantification, featuring comparative analysis against deterministic baselines.

🎯 Project Overview

This project implements a non-deterministic unsupervised neural network model using Variational Autoencoders for data generation on the MNIST dataset. The implementation includes comprehensive evaluation metrics, uncertainty quantification, and detailed analysis of the learned latent space representations.

Key Features

🧠 Stochastic VAE Architecture with reparameterization trick
📊 Comprehensive Evaluation including FID, clustering metrics, and uncertainty quantification
🎨 Data Generation capabilities with smooth latent space interpolation
📈 Baseline Comparison with deterministic autoencoder
🔬 Statistical Analysis with significance testing across multiple runs
📋 Professional Visualizations ready for research presentation

🚀 Quick Start

Prerequisites

# Required packages
torch>=1.13.0
torchvision>=0.14.0
numpy>=1.21.0
matplotlib>=3.5.0
scikit-learn>=1.1.0
seaborn>=0.11.0
scipy>=1.8.0

Installation

Clone the repository

git clone https://github.com/yourusername/vae-data-generation.git
cd vae-data-generation

Install dependencies

pip install -r requirements.txt

Run the complete pipeline

python main.py

Quick Demo

# Load pre-trained model and generate samples
from models.vae import VAE
import torch

# Load trained model
model = VAE(latent_dim=20)
model.load_state_dict(torch.load('models/best_vae_model.pth')['model_state_dict'])

# Generate new samples
samples = model.generate_samples(num_samples=16, device='cpu')

# Visualize results
from visualization.plots import visualize_samples
visualize_samples(samples)

📁 Project Structure

vae-data-generation/
├── data/                          # Dataset storage
│   ├── MNIST/                     # MNIST dataset files
│   └── preprocessing.py           # Data preprocessing utilities
├── models/                        # Model implementations
│   ├── vae.py                     # VAE model architecture
│   ├── baseline.py                # Deterministic baseline
│   └── utils.py                   # Model utilities
├── training/                      # Training scripts
│   ├── train_vae.py              # VAE training loop
│   ├── train_baseline.py         # Baseline training
│   └── early_stopping.py         # Early stopping implementation
├── evaluation/                    # Evaluation metrics
│   ├── metrics.py                # Comprehensive metrics
│   ├── uncertainty.py            # Uncertainty quantification
│   └── clustering.py             # Clustering analysis
├── visualization/                 # Visualization tools
│   ├── plots.py                  # Plotting functions
│   ├── latent_analysis.py        # Latent space analysis
│   └── interpolation.py          # Interpolation visualization
├── results/                       # Generated results
│   ├── figures/                  # All generated plots
│   ├── models/                   # Saved model checkpoints
│   └── metrics/                  # Evaluation results
├── notebooks/                     # Jupyter notebooks
│   ├── complete_implementation.ipynb  # Full implementation
│   ├── analysis.ipynb            # Results analysis
│   └── visualization.ipynb       # Visualization notebook
├── docs/                          # Documentation
│   ├── report.pdf                # Research report
│   └── architecture.md           # Model architecture details
├── main.py                        # Main execution script
├── requirements.txt               # Python dependencies
└── README.md                      # This file

🏗️ Model Architecture

Variational Autoencoder (VAE)

Input (784) → Encoder → μ, σ² (20) → Sampling → z (20) → Decoder → Output (784)
                ↓                      ↓
         [512, 256, 20]        z = μ + σ⊙ε        [20, 256, 512, 784]

Key Components:

Encoder: Maps input to latent parameters (μ, log σ²)
Reparameterization: Enables backpropagation through stochastic sampling
Decoder: Reconstructs input from latent representations
Loss Function: ELBO = Reconstruction Loss + β×KL Divergence

Architecture Details

Component	Layers	Activation	Parameters
Encoder	784→512→256→20×2	ReLU	543,528
Decoder	20→256→512→784	ReLU+Sigmoid	539,152
Total			1,082,680

📊 Results Summary

Performance Metrics

Metric	VAE	Baseline	Improvement
Reconstruction MSE	0.0447	0.0423	-5.7%
Generation Quality (FID)	12.3	N/A	✓
Silhouette Score	0.723	N/A	✓
Latent Utilization	85%	N/A	✓

Key Achievements

✅ High-quality sample generation with FID score of 12.3
✅ Excellent clustering with silhouette score of 0.723
✅ Efficient latent space with 85% dimension utilization
✅ Robust uncertainty quantification capabilities
✅ Smooth interpolation in learned representations

🖼️ Visual Results

Generated Samples

Latent Space Visualization

Training Curves

Interpolation Results

🔬 Evaluation Metrics

Implemented Metrics

Reconstruction Quality
- Mean Squared Error (MSE)
- Visual quality assessment
Generation Quality
- Fréchet Inception Distance (FID)
- Sample diversity analysis
Latent Space Analysis
- Silhouette Score
- Adjusted Rand Index (ARI)
- Normalized Mutual Information (NMI)
Uncertainty Quantification
- Reconstruction variance
- Confidence estimation
Statistical Validation
- Multiple run analysis
- Significance testing

🔧 Usage Examples

Training Custom VAE

from models.vae import VAE
from training.train_vae import train_model

# Initialize model
model = VAE(latent_dim=20, hidden_dims=[512, 256])

# Train model
history = train_model(
    model=model,
    train_loader=train_loader,
    val_loader=val_loader,
    epochs=100,
    learning_rate=1e-3,
    beta=1.0
)

Generating Samples

# Generate random samples
samples = model.generate_samples(num_samples=64)

# Generate with specific latent codes
z = torch.randn(16, 20)  # Custom latent codes
samples = model.decode(z)

Latent Space Interpolation

from visualization.interpolation import interpolate_samples

# Interpolate between two images
interpolated = interpolate_samples(
    model=model,
    start_image=image1,
    end_image=image2,
    num_steps=10
)

Uncertainty Analysis

from evaluation.uncertainty import quantify_uncertainty

# Analyze model uncertainty
uncertainty_results = quantify_uncertainty(
    model=model,
    test_loader=test_loader,
    num_samples=50
)

📚 Research Applications

This implementation is suitable for:

Academic Research in generative modeling
Data Augmentation for machine learning projects
Anomaly Detection using reconstruction error
Creative Applications with latent space manipulation
Uncertainty Quantification in neural networks
Educational Purposes for understanding VAEs

🤝 Contributing

Contributions are welcome! Please see our contributing guidelines for details.

Development Setup

# Clone repository
git clone https://github.com/yourusername/vae-data-generation.git
cd vae-data-generation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/

📄 Citation

If you use this implementation in your research, please cite:

@misc{vae_implementation_2025,
  author = {Your Name},
  title = {Non-Deterministic Variational Autoencoder for Data Generation},
  year = {2025},
  url = {https://github.com/yourusername/vae-data-generation},
  note = {Neural Networks Course Project}
}

📋 Requirements

System Requirements

Python 3.8+
CUDA-capable GPU (recommended)
4GB+ RAM
2GB+ storage space

Python Dependencies

PyTorch ≥ 1.13.0
torchvision ≥ 0.14.0
numpy ≥ 1.21.0
matplotlib ≥ 3.5.0
scikit-learn ≥ 1.1.0
scipy ≥ 1.8.0
seaborn ≥ 0.11.0

🐛 Troubleshooting

Common Issues

CUDA Out of Memory

# Reduce batch size
config['batch_size'] = 64  # Instead of 128

Slow Training

# Enable GPU acceleration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

Poor Generation Quality

# Adjust beta parameter
config['beta'] = 0.5  # Reduce regularization

📊 Benchmarks

Performance Comparison

Dataset	Model	MSE	FID	Training Time
MNIST	VAE (ours)	0.0447	12.3	15 min
MNIST	Standard VAE	0.0512	15.7	18 min
MNIST	β-VAE (β=2)	0.0389	11.8	16 min

🎓 Educational Resources

Learning Materials

Related Papers

Kingma & Welling (2013): Auto-Encoding Variational Bayes
Higgins et al. (2017): β-VAE: Learning Basic Visual Concepts
Rezende et al. (2014): Stochastic Backpropagation

🏆 Acknowledgments

MNIST Dataset: Yann LeCun et al.
PyTorch Team: For the deep learning framework
Research Community: For foundational VAE research
Course Instructor: For guidance and feedback

📞 Support

For questions or issues:

📧 Email: asheq100mahmud@gmail.com
💬 Discussions: GitHub Discussions

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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