List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

GAN-Based Synthetic Data Generation for Training Deep Learning Models on Google Cloud

Data Generation

Data Generation for Training Deep Learning Models on Google Cloud

  • Use Case : Many AI applications, like autonomous driving, healthcare imaging, and fraud detection, require large labeled datasets. Obtaining real data can be expensive, limited, or privacy-sensitive. Generative Adversarial Networks (GANs) can generate high-quality synthetic data to augment real datasets, improving model performance without compromising privacy or incurring high data collection costs.

Objective

  • Generate realistic synthetic data using GANs to augment existing datasets.

    Enable robust deep learning model training when real data is scarce or sensitive.

    Leverage Google Cloud’s scalable infrastructure for training computationally heavy GANs efficiently.

    Ensure synthetic data is privacy-preserving and suitable for downstream ML tasks.

Project Description

  • This project implements a GAN-based synthetic data generation pipeline on Google Cloud:

    Data Collection: Collect original datasets (e.g., medical images, sensor readings, financial transactions).

    Preprocessing: Normalize, resize, or encode data suitable for GAN training.

    GAN Training: Train GAN models (e.g., DCGAN, StyleGAN) on Vertex AI or AI Platform Notebooks.

    Synthetic Data Generation: Produce high-fidelity synthetic samples to augment or replace real data.

    Deep Learning Model Training: Use synthetic + real data to train downstream ML models for tasks like classification, anomaly detection, or forecasting.

    Evaluation: Compare model performance with and without synthetic data, measure fidelity and diversity of generated data.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud Storage Stores real and synthetic datasets, GAN checkpoints, and model outputs.
    Vertex AI / AI Platform Notebooks Train GAN models using scalable GPU/TPU resources; manage experiments and pipelines.
    Dataflow Preprocess and transform raw datasets into GAN-compatible formats.
    BigQuery Store structured datasets, training metadata, and evaluate synthetic vs real data distributions.
    Vertex AI Pipelines Orchestrates end-to-end GAN training, synthetic data generation, and downstream ML workflows.
    Cloud Functions Trigger synthetic data generation when new real datasets arrive.
    Cloud Monitoring / Logging Track model training metrics, resource usage, and system health.
    Cloud Key Management Service (KMS) Encrypt sensitive real datasets to maintain privacy and compliance.
    Looker / Data Studio Visualize generated data quality, coverage, and model performance improvements.