GANs (Generative Adversarial Networks) consist of a generator and discriminator that compete to produce realistic synthetic data. Use them for image generation, data augmentation, style transfer, and when you need high-fidelity synthetic outputs. Diffusion models are now often preferred for certain image tasks.

Diffusion models (Stable Diffusion, DALL·E) often give better quality and stability for image generation. GANs excel at fast inference, style transfer (CycleGAN), and data augmentation. We help you choose based on latency, quality, and use case.

Yes. We build discriminator-based classifiers and forensic detectors to identify synthetic or manipulated media. We use ensemble methods and explainability to improve robustness against evasion. We also help with policy and tooling for content moderation.

We use modern architectures (StyleGAN, BigGAN), spectral normalization, and training tricks (e.g., TTUR, gradient penalty). We monitor mode collapse and diversity metrics. For production, we often recommend hybrid approaches or diffusion when GAN instability is a blocker.

Yes. We build GANs for medical imaging, industrial defect synthesis, and domain-specific data augmentation. We ensure synthetic data preserves statistical properties and doesn't introduce bias. We integrate with your ML pipelines and MLOps workflows.

StyleGAN gives fine-grained control over generated images. CycleGAN enables unpaired image-to-image translation (e.g., photo→sketch, day→night). We implement both for creative and industrial use cases. We optimize for your hardware and latency requirements.

Prototype GAN projects take 4–6 weeks; production systems with custom data and optimization take 2–4 months. We provide iterative demos and can scale teams for parallel workstreams. We also offer ongoing maintenance and model updates.