RRepoGEO

REPOGEO REPORT · LITE

Guang000/Awesome-Dataset-Distillation

Default branch main · commit 9e3d1e69 · scanned 6/18/2026, 6:13:19 PM

GitHub: 1,944 stars · 179 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /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
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 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 the README's opening sentence to clarify it's a curated list

    Why:

    CURRENT
    **Awesome Dataset Distillation** provides the most comprehensive and detailed information on the Dataset Distillation field.
    COPY-PASTE FIX
    **Awesome Dataset Distillation** is a curated list of papers, code, and resources, providing the most comprehensive and detailed information on the Dataset Distillation field.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    awesome-list, deep-learning
    COPY-PASTE FIX
    awesome-list, deep-learning, dataset-distillation, machine-learning-resources, paper-list, survey
  • lowreadme#3
    Explicitly state the target audience in the README

    Why:

    COPY-PASTE FIX
    This curated list is an essential resource for researchers and practitioners interested in the latest advancements and applications of dataset distillation.

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
0%
Of all named tools, what % are you?
Top rival
scikit-learn/scikit-learn
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. scikit-learn/scikit-learn · recommended 2×
  2. scikit-learn-contrib/imbalanced-learn · recommended 2×
  3. pytorch/pytorch · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. t-SNE · recommended 1×
  • CATEGORY QUERY
    How can I shrink large datasets for faster deep learning model training?
    you: not recommended
    AI recommended (in order):
    1. Scikit-learn (scikit-learn/scikit-learn)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. t-SNE
    5. UMAP
    6. NumPy (numpy/numpy)
    7. HDF5
    8. h5py (h5py/h5py)
    9. Zarr (zarr-developers/zarr-python)
    10. Apache Parquet (apache/parquet-format)
    11. imbalanced-learn (scikit-learn-contrib/imbalanced-learn)
    12. Fast.ai (fastai/fastai)
    13. Cleanlab (cleanlab/cleanlab)
    14. modAL (cosmo-ethz/modAL)
    15. ALiPy (DataCanvasIO/ALiPy)

    AI recommended 15 alternatives but never named Guang000/Awesome-Dataset-Distillation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for distilling large datasets into smaller, representative ones?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. pandas (pandas-dev/pandas)
    3. scikit-learn (scikit-learn/scikit-learn)
    4. imbalanced-learn (scikit-learn-contrib/imbalanced-learn)
    5. R
    6. dplyr (tidyverse/dplyr)
    7. caret (topepo/caret)
    8. SQL
    9. stats
    10. cluster
    11. Java
    12. Apache DataSketches (apache/datasketches)
    13. Faiss (facebookresearch/faiss)
    14. Annoy (spotify/annoy)
    15. modAL (modAL-python/modAL)
    16. Rtsne (jkrijnen/Rtsne)
    17. umap (umap-learn/umap-r)

    AI recommended 17 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 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?

  • 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

Drop this badge into the README of Guang000/Awesome-Dataset-Distillation. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Guang000/Awesome-Dataset-Distillation.svg)](https://repogeo.com/en/r/Guang000/Awesome-Dataset-Distillation)
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

Subscribe to Pro for deep diagnoses

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
Guang000/Awesome-Dataset-Distillation — RepoGEO report