RRepoGEO

REPOGEO REPORT · LITE

podgorskiy/ALAE

Default branch master · commit 42bcf2e5 · scanned 6/25/2026, 5:27:53 PM

GitHub: 3,516 stars · 548 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface podgorskiy/ALAE, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., `LICENSE.md` or `LICENSE`) in the repository root with the full text of the Apache-2.0 license, as hinted by the broken link in the README.
  • highabout#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Set the homepage URL in the repository's 'About' section to `https://arxiv.org/abs/1912.04462` (the official paper link).
  • highreadme#3
    Add a concise problem-solution statement to the README introduction

    Why:

    CURRENT
    The current README starts with the paper title and abstract, followed by author information.
    COPY-PASTE FIX
    Add a sentence or two immediately after the H1, such as: 'This repository provides the official PyTorch implementation of Adversarial Latent Autoencoders (ALAE), a deep learning model for high-quality image synthesis, particularly effective for generating realistic human faces with disentangled latent representations.'

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface podgorskiy/ALAE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
StyleGAN
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. StyleGAN · recommended 1×
  2. DALL-E 2 · recommended 1×
  3. Midjourney · recommended 1×
  4. Stable Diffusion · recommended 1×
  5. ProGAN · recommended 1×
  • CATEGORY QUERY
    How can I generate realistic human faces using a deep learning model?
    you: not recommended
    AI recommended (in order):
    1. StyleGAN
    2. DALL-E 2
    3. Midjourney
    4. Stable Diffusion
    5. ProGAN
    6. BigGAN
    7. VQ-GAN
    8. CLIP
    9. GauGAN

    AI recommended 9 alternatives but never named podgorskiy/ALAE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good PyTorch implementations for adversarial autoencoders for image synthesis?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-GAN (eriklindernoren/PyTorch-GAN)
    2. AAE-PyTorch (yunjey/AAE-PyTorch)
    3. pytorch-generative-models (wiseodd/pytorch-generative-models)
    4. Adversarial-Autoencoders-PyTorch (lzhbrian/Adversarial-Autoencoders-PyTorch)
    5. Official PyTorch Examples

    AI recommended 5 alternatives but never named podgorskiy/ALAE. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of podgorskiy/ALAE?
    pass
    AI named podgorskiy/ALAE explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts podgorskiy/ALAE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named podgorskiy/ALAE explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo podgorskiy/ALAE solve, and who is the primary audience?
    pass
    AI named podgorskiy/ALAE explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of podgorskiy/ALAE. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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<a href="https://repogeo.com/en/r/podgorskiy/ALAE"><img src="https://repogeo.com/badge/podgorskiy/ALAE.svg" alt="RepoGEO" /></a>
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podgorskiy/ALAE — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite