Generative Models

Understanding and implementing generative models in deep learning

Generative Models

Generative models are a class of deep learning models capable of generating new data samples that resemble their training data.

Generative Adversarial Networks (GANs)

Basic Architecture

  • Generator network
  • Discriminator network
  • Adversarial training
  • Loss functions

GAN Variants

  • DCGAN
  • StyleGAN
  • CycleGAN
  • Conditional GANs
  • Progressive GANs

Training Challenges

  • Mode collapse
  • Training instability
  • Convergence issues
  • Evaluation metrics

Variational Autoencoders (VAEs)

Core Components

  • Encoder network
  • Decoder network
  • Latent space
  • Reparameterization trick

Architecture Types

  • Vanilla VAE
  • Conditional VAE
  • Beta-VAE
  • Hierarchical VAE

Applications

  • Image generation
  • Data compression
  • Anomaly detection
  • Style transfer

Diffusion Models

Principles

  • Forward diffusion
  • Reverse diffusion
  • Noise scheduling
  • Sampling strategies
  • DDPM
  • Stable Diffusion
  • Imagen
  • DALL-E

Applications

  • Image generation
  • Image editing
  • Inpainting
  • Super-resolution

Flow-based Models

Architecture

  • Invertible transformations
  • Change of variables
  • Exact likelihood
  • Normalizing flows

Types

  • NICE
  • RealNVP
  • Glow
  • Flow++

Autoregressive Models

Characteristics

  • Sequential generation
  • Conditional probability
  • Teacher forcing
  • Sampling strategies

Applications

  • Text generation
  • Image generation
  • Audio synthesis
  • Time series generation

Hybrid Approaches

VAE-GAN Hybrids

  • Joint training
  • Combined objectives
  • Architecture design
  • Performance benefits

Diffusion-Transformer Hybrids

  • Text-to-image models
  • Controlled generation
  • Multi-modal synthesis
  • Compositional generation

Training Techniques

Loss Functions

  • Adversarial loss
  • Reconstruction loss
  • KL divergence
  • Perceptual loss

Optimization

  • Learning rate scheduling
  • Gradient penalty
  • Progressive growing
  • Curriculum learning

Best Practices

  1. Model selection
  2. Data preparation
  3. Training stability
  4. Evaluation methods
  5. Quality assessment

Implementation Challenges

  • Computational resources
  • Training time
  • Mode collapse
  • Sample quality
  • Evaluation metrics

Tools and Frameworks

  • PyTorch
  • TensorFlow
  • JAX
  • Diffusers
  • Lightning
  • Neural Networks
  • Optimization Methods
  • Computer Vision
  • Deep Learning Theory