REPOGEO REPORT · LITE

YU-deep/Awesome-Latent-Space

Default branch main · commit 806b36bb · scanned 6/8/2026, 10:02:53 PM

GitHub: 901 stars · 35 forks

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 YU-deep/Awesome-Latent-Space, 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 H1 and opening sentence to clarify the repo's primary function

Why:

CURRENT

H1: The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
First sentence: This repository manually collects works in **latent space**, which will be continuously updated.

COPY-PASTE FIX

H1: Awesome Latent Space: A Curated Collection of Papers and Resources
First sentence: This repository is an awesome list manually collecting works in **latent space**, continuously updated, and includes our comprehensive survey: 'The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook'.

mediumhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2604.02029
```

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 YU-deep/Awesome-Latent-Space

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Generative Adversarial Networks: A Survey by Gui et al. (2020)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Generative Adversarial Networks: A Survey by Gui et al. (2020) · recommended 1×
Variational Autoencoders and Generative Models: A Survey by Kingma and Welling (2019) · recommended 1×
Disentangled Representation Learning: A Review by Bengio et al. (2019) · recommended 1×
β-VAE · recommended 1×
FactorVAE · recommended 1×

CATEGORY QUERY
Looking for a comprehensive survey or overview of the current state of latent space research.
you: not recommended
AI recommended (in order):
1. Generative Adversarial Networks: A Survey by Gui et al. (2020)
2. Variational Autoencoders and Generative Models: A Survey by Kingma and Welling (2019)
3. Disentangled Representation Learning: A Review by Bengio et al. (2019)
4. β-VAE
5. FactorVAE
6. InfoGAN
7. Deep Learning for Medical Image Analysis: A Review by Litjens et al. (2017)
8. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks by Frankle and Carbin (2019)
9. Transformers: A Survey by Khan et al. (2022)
10. Graph Neural Networks: A Review of Methods and Applications by Zhou et al. (2020)
AI recommended 10 alternatives but never named YU-deep/Awesome-Latent-Space. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find a curated collection of foundational and recent papers on latent space?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. arXiv
3. Google Scholar
4. Distill.pub
5. GitHub
6. r/MachineLearning
7. r/deeplearning
8. NeurIPS
9. ICML
10. ICLR
11. CVPR
12. ICCV
13. AAAI
AI recommended 13 alternatives but never named YU-deep/Awesome-Latent-Space. 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 YU-deep/Awesome-Latent-Space?
pass
AI named YU-deep/Awesome-Latent-Space explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts YU-deep/Awesome-Latent-Space in production, what risks or prerequisites should they evaluate first?
pass
AI named YU-deep/Awesome-Latent-Space 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 YU-deep/Awesome-Latent-Space solve, and who is the primary audience?
pass
AI did not name YU-deep/Awesome-Latent-Space — 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

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/YU-deep/Awesome-Latent-Space.svg)](https://repogeo.com/en/r/YU-deep/Awesome-Latent-Space)

HTML

<a href="https://repogeo.com/en/r/YU-deep/Awesome-Latent-Space"><img src="https://repogeo.com/badge/YU-deep/Awesome-Latent-Space.svg" alt="RepoGEO" /></a>

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YU-deep/Awesome-Latent-Space — 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