Synthetic Data Generation

Generate privacy-safe, high-fidelity synthetic data.

Overview

Synthetic data replicates real-world distributions—facilitating privacy compliance, addressing class imbalance, and enabling rapid model iteration without exposing sensitive information.

State-of-the-Art Methods and Architectures

GANs

StyleGAN2/3 for high-fidelity imagery, conditional GANs for targeted classes.

Diffusion Models

Stable Diffusion, Imagen for controllable text-to-image synthesis.

VAEs

Efficient at low-dimensional representation learning.

LLM-based Data Augmentation

Generate labeled text samples for NLP tasks.

Market Landscape & Forecasts

80%

Healthcare Adoption

AI-assisted diagnosis

Simulated Data

Autonomous Driving

PII-free logs

Finance

Implementation Guide

Select Generation Model

GAN vs. diffusion vs. VAE based on output fidelity needs.

Train on Real Data

Feed model representative samples (e.g., 10,000 images).

Quality Assessment

Use Frechet Inception Distance (FID) and human evaluation.

Integration

Blend synthetic and real data sets in pipelines with class weighting.

Technical Deep Dive

Data Preparation

Collect domain-specific text (e.g., medical records, legal documents). Clean and format data into JSONL.

Adapter Insertion

Insert LoRA/QLoRA adapters into the base model.

Training

Run training with domain data, using a learning rate schedule and early stopping. Monitor loss and validation metrics.

Evaluation

Use ROUGE, accuracy, or custom metrics. Compare outputs to base model.

Sample Code

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer model = AutoModelForCausalLM.from_pretrained('llama-7b') # Insert LoRA adapters... # Prepare data... trainer = Trainer(model=model, args=TrainingArguments(...), train_dataset=...) trainer.train()

Why Fine-Tuning?

Real Data Only

- Privacy risk - Class imbalance - Limited data volume

Synthetic + Real Data

- Privacy-safe - Balanced classes - Unlimited augmentation

FAQ

Industry Voices

"Synthetic data enables privacy-safe AI innovation."

AI Privacy Report, 2024

Project Timeline

Model Selection

Choose GAN, diffusion, or VAE.

Training

Train on real data.

Synthesis

Generate synthetic samples.

Synthetic Data Generation

Overview

State-of-the-Art Methods and Architectures

Market Landscape & Forecasts

Implementation Guide

Technical Deep Dive

Data Preparation

Adapter Insertion

Training

Evaluation

Sample Code

Why Fine-Tuning?

FAQ

Industry Voices

Project Timeline

Generate Synthetic Data