HyperGAN
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  • Getting started
  • CLI guide
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    • Configurable Parameters
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  • Examples
    • 2D
    • Text
    • Classification
    • Colorizer
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  • Tutorials
    • Training a GAN
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    • 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
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      • Accumulate Gradient Trainer
    • Optimizer
    • Train Hook
      • Adversarial Norm
      • Weight Constraint
      • Stabilizing Training
      • JARE
      • Learning Rate Dropout
      • Gradient Penalty
      • Rolling Memory
    • Other GAN implementations
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  1. Components
  2. Loss

Realness Loss

  • Adapted from https://github.com/kam1107/RealnessGAN/

  • Source: /losses/realness_loss.py

examples

  • Configurations: /losses/realness_loss/

{
  "class": "class:hypergan.losses.realness_loss.RealnessLoss",
  "skew": [-0.1, 0.1]
}

options

attribute

description

type

skew

array of floats

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Last updated 4 years ago

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