Synthetic & Generative Data
Creating Unlimited High-Quality Training Data
Solve data scarcity with AI that generates realistic datasets
data generation
privacy protection
cost reduction
Generative Technologies
Generative Adversarial Networks
AI systems using adversarial competition between Generator and Discriminator to create realistic data
Classic GANs
- • DCGAN - Deep Convolutional GAN
- • WGAN - Wasserstein GAN
- • WGAN-GP - with Gradient Penalty
- • LSGAN - Least Squares GAN
Advanced GANs
- • StyleGAN - Style-based generation
- • CycleGAN - Unpaired translation
- • Pix2Pix - Image-to-image
- • BigGAN - Large-scale generation
Diffusion Models
Models that generate data by gradually removing noise, producing high-quality, controllable results
Core Models
- • DDPM - Denoising Diffusion
- • DDIM - Implicit Diffusion
- • Stable Diffusion - Latent space
- • Imagen - Text-to-image
Specialized
- • ControlNet - Guided generation
- • Inpainting models
- • Super-resolution diffusion
- • Video diffusion models
Variational Autoencoders
Models that learn latent representations and generate new data through probabilistic sampling
Standard VAE
- • β-VAE - Controlled disentanglement
- • InfoVAE - Information preservation
- • WAE - Wasserstein Autoencoder
- • VQ-VAE - Vector Quantized
Hierarchical
- • Ladder VAE - Multi-scale
- • ConvDRAW - Sequential generation
- • BIGAN - Bidirectional
- • ALI - Adversarially Learned
Normalizing Flows
Models using invertible transformations to learn exact complex data distributions
Coupling Flows
- • RealNVP - Real-valued NVP
- • Glow - Invertible 1x1 convs
- • Flow++ - Improved coupling
- • WaveGlow - Audio generation
Autoregressive
- • MAF - Masked Autoregressive
- • IAF - Inverse Autoregressive
- • Neural Spline Flows
- • Continuous normalizing flows
Industrial Use Cases
Defect Simulation
Generate rare defect data for quality inspection system training
- Surface cracks
- Stains and discoloration
- Mechanical damage
Environment Simulation
Generate data under diverse environmental conditions difficult to capture
- Lighting variations
- Extreme weather
- Hazardous conditions
Equipment Aging
Simulate equipment degradation processes over operational lifetime
- Bearing wear
- Sensor degradation
- Corrosion progression
Safety Scenarios
Generate dangerous scenario data that cannot be safely simulated
- Fire incidents
- Chemical leaks
- Equipment explosions
Product Variations
Generate product data in diverse configurations and variations
- New shapes and sizes
- Color and material variations
- Customer customizations
Privacy Protection
Replace sensitive data with safe synthetic alternatives
- Customer data protection
- Process data anonymization
- GDPR compliance
Implementation Strategy
Implementation Steps
Assess Data Needs
Analyze data gaps and specific requirements
Select Technology
Choose appropriate generative model for data characteristics
Train & Fine-tune
Train model with real data and fine-tune for specific use case
Quality Validation
Evaluate quality and realism of generated data
Evaluation Metrics
Realism Assessment
FID, IS, LPIPS scores
Diversity Measurement
Coverage, precision-recall curves
Training Efficacy
Downstream task performance
Privacy Protection
Membership inference resistance
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