RRepoGEO

REPOGEO REPORT · LITE

dev-sungman/Awesome-Self-Supervised-Papers

Default branch master · commit a6f97b98 · scanned 6/6/2026, 4:58:19 PM

GitHub: 587 stars · 57 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 dev-sungman/Awesome-Self-Supervised-Papers, 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
    Emphasize unique value proposition in README's opening

    Why:

    CURRENT
    Collecting papers about Self-Supervised Learning, Representation Learning.
    COPY-PASTE FIX
    A curated and comprehensive collection of influential Self-Supervised Learning and Representation Learning papers, featuring concise summaries, key insights, and links to official/unofficial code implementations where available.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects the intended usage of the paper list.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to a relevant project page, blog post, or a direct link to the GitHub Pages if one is set up for the repo.

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 dev-sungman/Awesome-Self-Supervised-Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Self-Supervised Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Self-Supervised Learning · recommended 1×
  2. Papers With Code · recommended 1×
  3. arXiv.org · recommended 1×
  4. Google Scholar · recommended 1×
  5. Distill.pub · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of influential self-supervised learning papers?
    you: not recommended
    AI recommended (in order):
    1. Awesome Self-Supervised Learning
    2. Papers With Code
    3. arXiv.org
    4. Google Scholar
    5. Distill.pub
    6. NeurIPS
    7. ICML
    8. ICLR
    9. CVPR
    10. ICCV
    11. ACL
    12. EMNLP

    AI recommended 12 alternatives but never named dev-sungman/Awesome-Self-Supervised-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Show me research papers on self-supervised learning methods using distillation techniques.
    you: not recommended
    AI recommended (in order):
    1. BYOL
    2. SimSiam
    3. SwAV
    4. DINO
    5. MAE
    6. Barlow Twins
    7. BYOL-A

    AI recommended 7 alternatives but never named dev-sungman/Awesome-Self-Supervised-Papers. 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 dev-sungman/Awesome-Self-Supervised-Papers?
    pass
    AI named dev-sungman/Awesome-Self-Supervised-Papers explicitly

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

  • If a team adopts dev-sungman/Awesome-Self-Supervised-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dev-sungman/Awesome-Self-Supervised-Papers 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 dev-sungman/Awesome-Self-Supervised-Papers solve, and who is the primary audience?
    pass
    AI did not name dev-sungman/Awesome-Self-Supervised-Papers — 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 dev-sungman/Awesome-Self-Supervised-Papers. 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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dev-sungman/Awesome-Self-Supervised-Papers — 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