REPOGEO REPORT · LITE

pfnet-research/sngan_projection

Default branch master · commit e84b1a5f · scanned 5/19/2026, 11:23:01 AM

GitHub: 1,104 stars · 201 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

22 /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

1 / 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 pfnet-research/sngan_projection, 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

2 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 clarify its research focus

Why:

CURRENT

GANs with spectral normalization and projection discriminator
*NOTE: The setup and example code in this README are for training GANs on **single GPU**.*
*The models are smaller than the ones used in the papers.*
*Please go to **link** if you are looking for how to reproduce the results in the papers.*

Official Chainer implementation for conditional image generation on ILSVRC2012 dataset (ImageNet) with [spectral normalization][sngans] and [projection discrimiantor][pcgans].

COPY-PASTE FIX

This repository provides the official Chainer implementation for conditional image generation on the ILSVRC2012 dataset (ImageNet), showcasing advanced Generative Adversarial Network (GAN) architectures: **spectral normalization** and a **projection discriminator**. It is designed for researchers and practitioners interested in the underlying mechanics and implementation details of these specific GAN techniques.

mediumreadme#2
Clarify the existing license in the README
Why:
COPY-PASTE FIX
```
## License
This project is licensed under the terms specified in the `LICENSE` file.
```

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 pfnet-research/sngan_projection

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Stable Diffusion

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Stable Diffusion · recommended 2×
Midjourney · recommended 2×
ControlNet · recommended 2×
StyleGAN3 · recommended 1×
StyleGAN2-ADA · recommended 1×

CATEGORY QUERY
How to generate high-resolution conditional images using advanced GAN architectures?
you: not recommended
AI recommended (in order):
1. StyleGAN3
2. StyleGAN2-ADA
3. Stable Diffusion
4. DALL-E 2
5. Midjourney
6. BigGAN
7. GauGAN
8. Pix2PixHD
9. ControlNet
AI recommended 9 alternatives but never named pfnet-research/sngan_projection. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Need a library for generating realistic images conditioned on specific categories.
you: not recommended
AI recommended (in order):
1. Stable Diffusion
2. DALL-E 3
3. Midjourney
4. ControlNet
5. Imagen
AI recommended 5 alternatives but never named pfnet-research/sngan_projection. 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 pfnet-research/sngan_projection?
pass
AI did not name pfnet-research/sngan_projection — 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?
If a team adopts pfnet-research/sngan_projection in production, what risks or prerequisites should they evaluate first?
pass
AI named pfnet-research/sngan_projection 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 pfnet-research/sngan_projection solve, and who is the primary audience?
pass
AI did not name pfnet-research/sngan_projection — 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?

Embed your GEO score

Drop this badge into the README of pfnet-research/sngan_projection. 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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HTML

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Pro

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pfnet-research/sngan_projection — 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