RRepoGEO

REPOGEO REPORT · LITE

junkunyuan/Awesome-Domain-Generalization

Default branch main · commit 0dc60d7b · scanned 6/3/2026, 5:57:45 AM

GitHub: 534 stars · 53 forks

AI VISIBILITY SCORE
52 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 junkunyuan/Awesome-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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root containing the text of a standard open-source license (e.g., MIT, Apache 2.0, GPLv3) that best suits the project.
  • highreadme#2
    Strengthen README's opening sentence to emphasize code and datasets

    Why:

    CURRENT
    This repository is a collection of awesome things about **domain generalization**, including papers, code, etc.
    COPY-PASTE FIX
    This repository is a comprehensive collection of awesome resources for **domain generalization**, including key research papers, code implementations, and benchmark datasets.
  • mediumhomepage#3
    Add a homepage URL in the repository settings

    Why:

    COPY-PASTE FIX
    Set the homepage URL in the repository settings to https://github.com/junkunyuan/Awesome-Domain-Generalization

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
1 / 2
50% of queries surface junkunyuan/Awesome-Domain-Generalization
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
zhaoxin94/Awesome-Domain-Adaptation
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. zhaoxin94/Awesome-Domain-Adaptation · recommended 1×
  2. Papers with Code · recommended 1×
  3. NeurIPS · recommended 1×
  4. ICML · recommended 1×
  5. ICLR · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources for deep learning domain generalization techniques?
    you: #2
    AI recommended (in order):
    1. Awesome-Domain-Adaptation (zhaoxin94/Awesome-Domain-Adaptation)
    2. Awesome-Domain-Generalization (jindongwang/transferlearning) ← you
    3. Papers with Code
    4. NeurIPS
    5. ICML
    6. ICLR
    7. CVPR
    8. ICCV
    9. ECCV
    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source libraries and datasets for robust domain generalization research?
    you: not recommended
    AI recommended (in order):
    1. DomainBed
    2. Pytorch-Adapt
    3. MMDetection
    4. MMSegmentation
    5. MMClassification
    6. Higher
    7. Wilds
    8. DomainNet
    9. PACS
    10. Office-Home
    11. VLCS
    12. TerraIncognita
    13. Camelyon17
    14. PovertyMap
    15. RxRx1
    16. Amazon

    AI recommended 16 alternatives but never named junkunyuan/Awesome-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 junkunyuan/Awesome-Domain-Generalization?
    pass
    AI did not name junkunyuan/Awesome-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?

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

Embed your GEO score

Drop this badge into the README of junkunyuan/Awesome-Domain-Generalization. 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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junkunyuan/Awesome-Domain-Generalization — 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