RRepoGEO

REPOGEO REPORT · LITE

amber0309/Domain-generalization

Default branch master · commit 3a03bb30 · scanned 6/3/2026, 10:38:14 PM

GitHub: 846 stars · 123 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 amber0309/Domain-generalization, 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 to specify it's a curated collection

    Why:

    CURRENT
    # Domain generalization
    COPY-PASTE FIX
    # Domain Generalization: A Curated Collection of Research Papers and Datasets
  • mediumabout#2
    Update the repository description for clarity

    Why:

    CURRENT
    All about domain generalization
    COPY-PASTE FIX
    A curated collection of research papers, survey papers, and datasets focused on domain generalization in machine learning and computer vision.

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 amber0309/Domain-generalization
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Domain Generalization
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Domain Generalization · recommended 1×
  2. Papers With Code · recommended 1×
  3. arXiv · recommended 1×
  4. Google Scholar · recommended 1×
  5. NeurIPS · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of recent research papers on domain generalization?
    you: not recommended
    AI recommended (in order):
    1. Awesome Domain Generalization
    2. Papers With Code
    3. arXiv
    4. Google Scholar
    5. NeurIPS
    6. ICML
    7. ICLR
    8. CVPR
    9. ICCV
    10. ECCV
    11. Towards Data Science
    12. Analytics Vidhya

    AI recommended 12 alternatives but never named amber0309/Domain-generalization. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the essential survey papers and datasets for studying domain generalization methods?
    you: not recommended
    AI recommended (in order):
    1. DomainBed
    2. MAML
    3. PACS
    4. VLCS
    5. Office-Home
    6. DomainNet
    7. TerraIncognita
    8. Wilds
    9. Camelyon17
    10. iWildCam

    AI recommended 10 alternatives but never named amber0309/Domain-generalization. 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 amber0309/Domain-generalization?
    pass
    AI named amber0309/Domain-generalization explicitly

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

  • If a team adopts amber0309/Domain-generalization in production, what risks or prerequisites should they evaluate first?
    pass
    AI named amber0309/Domain-generalization 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 amber0309/Domain-generalization solve, and who is the primary audience?
    pass
    AI did not name amber0309/Domain-generalization — 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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