Training Techniques and Optimization Strategies for Generative Models

Advanced

🛠️ Effective Training of Generative Models

Effective training of generative models requires specialized techniques to ensure convergence and data fidelity:

🤼‍♂️ Adversarial Training (GANs)

Stability is often an issue. Techniques like:

  • ✅ Feature matching
  • ✅ Label smoothing
  • ✅ Wasserstein distance with gradient penalty
    help improve training stability.

🌀 VAE Optimization

Uses the evidence lower bound (ELBO) to balance:

  • 🧩 Reconstruction loss
  • 🧠 KL divergence

This promotes meaningful latent spaces.

🧠 Transformer Training

Large models are trained using:

  • 🔒 Masked language modeling
  • 🔁 Autoregressive objectives

Training leverages massive datasets and GPU clusters.

🧰 Best Practices

  • 🔄 Data normalization and augmentation to boost model robustness
  • 📈 Progressive training strategies, like progressively growing GANs for high-res images
  • 🛡️ Regularization techniques to prevent mode collapse and overfitting

💻 Example: Gradient Penalty in WGAN-GP

# Example: Gradient Penalty term in WGAN-GP
def gradient_penalty(discriminator, real, fake, device):
    alpha = torch.rand(real.size(0), 1, 1, 1, device=device)
    interpolated = alpha * real + (1 - alpha) * fake
    interpolated.requires_grad_(True)
    prob_interpolated = discriminator(interpolated)
    gradients = torch.autograd.grad(
        outputs=prob_interpolated,
        inputs=interpolated,
        grad_outputs=torch.ones_like(prob_interpolated),
        create_graph=True,
        retain_graph=True
    )[0]
    gradient_norm = gradients.view(gradients.size(0), -1).norm(2, dim=1)
    penalty = ((gradient_norm - 1) ** 2).mean()
    return penalty