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생산성 및 협업 · SKILL.md

vae

vae

Routes legacy VAE-family MNIST scripts, backend choices, model variant lookup, and TensorFlow/PyTorch compatibility troubleshooting.

Independent GitHub snapshot · Automated classification · Original documentation governs

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GitHub

name: vae description: "Routes legacy VAE-family MNIST scripts, backend choices, model variant lookup, and TensorFlow/PyTorch compatibility troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Unlicense

VAE

Use this sub-skill for VAE-family requests in the Generative Models repository: vanilla VAE, conditional VAE, denoising VAE, adversarial autoencoder, and adversarial variational Bayes.

Route here when

  • The user asks which VAE variant or source artifact label matches a modeling goal.
  • The user needs TensorFlow-vs-PyTorch guidance for a legacy VAE example.
  • The user asks about MNIST inputs, generated image outputs, latent-variable assumptions, or why a VAE script fails on a modern stack.
  • The user names CVAE, DVAE, AAE, or AVB.

Route elsewhere when

  • GAN variants: ../gan/SKILL.md
  • Binary RBM CD/PCD: ../rbm/SKILL.md
  • Helmholtz Machine wake-sleep: ../helmholtz-machine/SKILL.md
  • Shared catalog, provenance, and compatibility: ../../SKILL.md

Fast decisions

  • Default VAE baseline: vanilla VAE.
  • Class-conditioned generation: conditional VAE.
  • Denoising / corrupted input reconstruction: denoising VAE.
  • Adversarial latent regularization: adversarial autoencoder.
  • Adversarial variational objective with a T network: adversarial variational Bayes.

Shared conventions

  • These are standalone legacy MNIST training loops, not a packaged library or CLI.
  • The TensorFlow branches use old placeholder/session style.
  • The PyTorch branches still rely on the legacy TensorFlow MNIST loader and old scalar logging patterns.
  • Sample images are written to a working-directory-local out/ directory in source checkouts.

Read next

  • references/workflows.md for the full VAE variant map and framework coverage.
  • references/troubleshooting.md for VAE-specific modern-stack failures.
  • ../../references/model-catalog.md for the repo-wide source artifact catalog.
  • ../../references/compatibility.md for the shared TensorFlow/PyTorch/NumPy compatibility matrix.
  • ../../scripts/check_legacy_stack.py before claiming an unchanged VAE example can execute.
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