# SummitKwan/transparent_latent_gan

Use supervised learning to illuminate the latent space of GAN for controlled generation and edit

Repository: https://github.com/SummitKwan/transparent_latent_gan
Canonical: https://ross.abutalabs.com/products/transparent_latent_gan
Language: Python
License: MIT
License Family: permissive
Topics: tl-gan, tensorflow, keras, python, gan
Last push: 2020-12-28T10:47:45+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2912, "days_push": 2074, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1973, forks 356 (observed 2026-08-28T04:06:01.070195+00:00)

## What it is
TL-GAN is a Python/TensorFlow project that makes a GAN's latent space transparent by discovering feature axes, enabling controlled image synthesis and editing. It uses a coupled feature extractor network to correlate latent vectors with image features without retraining the GAN.

## Use cases
- control features of GAN-generated images
- edit generated faces along attributes like age or gender
- add controllable knobs to a pretrained GAN without retraining
- explore and interpret a GAN's latent space
- generate photo-realistic images with specified traits

## When to choose
- you have a pretrained GAN and want feature-controlled generation quickly
- you want to add new attribute controllers without retraining the generator
- you want an interpretable latent space for image editing experiments

## When to avoid
- you need a maintained, production-ready image generation pipeline
- you want state-of-the-art GAN architectures like StyleGAN with native controls
- you need non-Python or non-TensorFlow 1.x environments

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, machine-learning, image-processing, computer-vision
- platform: python
- tags: gan, latent-space, controlled-generation, tensorflow, keras, image-synthesis, research, gpu

## Member repositories
- SummitKwan/transparent_latent_gan (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:01.070195+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:04:57.408072+00:00, confidence not recorded.
  - readme: https://github.com/SummitKwan/transparent_latent_gan (fetched 2026-08-28T04:06:01.070195+00:00, sha e51f673033fa)
- Data as of 2026-08-30T08:39:29.467469+00:00.