REPOGEO REPORT · LITE

Guang000/Awesome-Dataset-Distillation

Default branch main · commit 675fd418 · scanned 5/8/2026, 11:29:23 PM

GitHub: 1,930 stars · 178 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 Guang000/Awesome-Dataset-Distillation, 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

Reposition README opening to clarify "awesome list" nature

Why:

CURRENT

Awesome Dataset Distillation provides the most comprehensive and detailed information on the Dataset Distillation field.

COPY-PASTE FIX

This repository, **Awesome Dataset Distillation**, is a curated list of awesome papers, code, and resources on dataset distillation and related applications. It provides the most comprehensive and detailed information on the Dataset Distillation field.

mediumtopics#2

Add more specific topics for dataset distillation techniques

Why:

CURRENT

awesome-list, deep-learning

COPY-PASTE FIX

awesome-list, dataset-distillation, dataset-condensation, gradient-matching, model-compression, machine-learning, deep-learning

lowabout#3

Enhance repository description for clarity and audience

Why:

CURRENT

A curated list of awesome papers on dataset distillation and related applications.

COPY-PASTE FIX

A curated list of awesome papers, code, and resources on dataset distillation and related applications, serving as a comprehensive guide for researchers and practitioners in machine learning.

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 Guang000/Awesome-Dataset-Distillation

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Faker

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Faker · recommended 1×
Synthetic Data Vault (SDV) · recommended 1×
PyTorch · recommended 1×
TensorFlow · recommended 1×
Albumentations · recommended 1×

CATEGORY QUERY
How to create smaller synthetic datasets for faster deep learning model development?
you: not recommended
AI recommended (in order):
1. Faker
2. Synthetic Data Vault (SDV)
3. PyTorch
4. TensorFlow
5. Albumentations
6. Keras ImageDataGenerator
7. Scikit-learn
8. NumPy
9. Pandas
AI recommended 9 alternatives but never named Guang000/Awesome-Dataset-Distillation. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What techniques exist for distilling large datasets to improve training efficiency and privacy?
you: not recommended
AI recommended (in order):
1. Dataset Distillation (DD)
2. Dataset Condensation (DC)
3. Gradient Matching (GM)
4. Coresets
5. Influence Functions
6. Active Learning
7. DP-SGD (Differentially Private Stochastic Gradient Descent)
8. PATE (Private Aggregation of Teacher Ensembles)
9. Laplacian Mechanism
10. Gaussian Mechanism
11. Knowledge Distillation (KD)
12. Hinton's KD (Soft Targets)
13. FitNets
AI recommended 13 alternatives but never named Guang000/Awesome-Dataset-Distillation. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
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 Guang000/Awesome-Dataset-Distillation?
pass
AI named Guang000/Awesome-Dataset-Distillation explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Guang000/Awesome-Dataset-Distillation in production, what risks or prerequisites should they evaluate first?
pass
AI named Guang000/Awesome-Dataset-Distillation 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 Guang000/Awesome-Dataset-Distillation solve, and who is the primary audience?
pass
AI did not name Guang000/Awesome-Dataset-Distillation — 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)

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HTML

<a href="https://repogeo.com/en/r/Guang000/Awesome-Dataset-Distillation"><img src="https://repogeo.com/badge/Guang000/Awesome-Dataset-Distillation.svg" alt="RepoGEO" /></a>

Pro

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Guang000/Awesome-Dataset-Distillation — 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