RRepoGEO

REPOGEO REPORT · LITE

yfeng95/GAN

Default branch master · commit 45bde57d · scanned 6/20/2026, 11:27:27 PM

GitHub: 3,099 stars · 797 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)

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

AI VISIBILITY SCORE
28 /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
2 / 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 yfeng95/GAN, 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
  • highreadme#1
    Reposition the README's opening statement to highlight its role as a code reference.

    Why:

    CURRENT
    I organized this reposity mainly for learning GANs, so all codes about classical GANs were implemented with simple network structure and tested by MNIST dataset.
    COPY-PASTE FIX
    This repository provides clean, well-structured, and functional reference implementations for foundational Generative Adversarial Network (GAN) architectures, including GAN, DCGAN, WGAN, CGAN, and InfoGAN. Each model is implemented with a simple network structure and tested on the MNIST dataset, making it an ideal resource for understanding how mathematical analyses translate into practical code.
  • highlicense#2
    Add a LICENSE file to the repository.

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) to clarify usage rights.
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section.

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the repository's homepage field in the GitHub 'About' section. This could be a project website, a related paper, or a blog post providing more context about the GAN implementations.

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 yfeng95/GAN
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow's official GAN tutorials
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow's official GAN tutorials · recommended 1×
  2. keras-team/keras-examples · recommended 1×
  3. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster · recommended 1×
  4. Generative Adversarial Networks (GANs) Specialization on Coursera · recommended 1×
  5. GitHub Repositories (Community) · recommended 1×
  • CATEGORY QUERY
    How to get started with implementing basic generative adversarial networks using TensorFlow?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow's official GAN tutorials
    2. Keras Examples (keras-team/keras-examples)
    3. Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster
    4. Generative Adversarial Networks (GANs) Specialization on Coursera
    5. GitHub Repositories (Community)

    AI recommended 5 alternatives but never named yfeng95/GAN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find Python code examples for DCGAN, WGAN, or Conditional GANs?
    you: not recommended
    AI recommended (in order):
    1. Keras
    2. PyTorch
    3. TensorFlow
    4. GitHub
    5. Papers With Code
    6. Machine Learning Mastery

    AI recommended 6 alternatives but never named yfeng95/GAN. 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 yfeng95/GAN?
    pass
    AI named yfeng95/GAN explicitly

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

  • If a team adopts yfeng95/GAN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yfeng95/GAN 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 yfeng95/GAN solve, and who is the primary audience?
    pass
    AI did not name yfeng95/GAN — likely talking about a different project

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

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yfeng95/GAN — 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