HyperGAN
  • About
  • Getting started
  • CLI guide
  • Configurations
    • Configurable Parameters
  • Showcase
    • AI Explorer for Android
    • Youtube, Twitter, Discord +
  • Examples
    • 2D
    • Text
    • Classification
    • Colorizer
    • Next Frame (video)
  • Tutorials
    • Training a GAN
    • Pygame inference
    • Creating an image dataset
    • Searching for hyperparameters
  • Components
    • GAN
      • Aligned GAN
      • Aligned Interpolated GAN
      • Standard GAN
    • Generator
      • Configurable Generator
      • DCGAN Generator
      • Resizable Generator
    • Discriminator
      • DCGAN Discriminator
      • Configurable Discriminator
    • Layers
      • add
      • cat
      • channel_attention
      • ez_norm
      • layer
      • mul
      • multi_head_attention
      • operation
      • pixel_shuffle
      • residual
      • resizable_stack
      • segment_softmax
      • upsample
    • Loss
      • ALI Loss
      • F Divergence Loss
      • Least Squares Loss
      • Logistic Loss
      • QP Loss
      • RAGAN Loss
      • Realness Loss
      • Softmax Loss
      • Standard Loss
      • Wasserstein Loss
    • Latent
      • Uniform Distribution
    • Trainer
      • Alternating Trainer
      • Simultaneous Trainer
      • Balanced Trainer
      • Accumulate Gradient Trainer
    • Optimizer
    • Train Hook
      • Adversarial Norm
      • Weight Constraint
      • Stabilizing Training
      • JARE
      • Learning Rate Dropout
      • Gradient Penalty
      • Rolling Memory
    • Other GAN implementations
Powered by GitBook
On this page

Was this helpful?

  1. Components
  2. GAN

Aligned Interpolated GAN

Discover new datasets that exist between two distributions.

PreviousAligned GANNextStandard GAN

Last updated 4 years ago

Was this helpful?