RRepoGEO

REPOGEO REPORT · LITE

Vanint/Awesome-LongTailed-Learning

Default branch main · commit 2c1ad2a6 · scanned 5/21/2026, 8:08:42 PM

GitHub: 1,019 stars · 127 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Vanint/Awesome-LongTailed-Learning, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    awesome-list, long-tailed-learning, deep-learning, machine-learning, computer-vision, survey, research, imbalanced-data
  • highreadme#2
    Clarify the README's opening to emphasize its 'awesome list' and survey nature

    Why:

    CURRENT
    # Awesome Long-Tailed Learning (TPAMI 2023)

We released *Deep Long-Tailed Learning: A Survey* and **our codebase** to the community.
    COPY-PASTE FIX
    # Awesome Long-Tailed Learning (TPAMI 2023)

This repository serves as a comprehensive **awesome list** and **codebase** accompanying our TPAMI 2023 survey, *Deep Long-Tailed Learning: A Survey*. It curates recent advances, provides empirical analysis, and offers resources for long-tailed learning based on deep neural networks.
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root, or explicitly state the licensing terms in the README if a custom license is intended.

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 Vanint/Awesome-LongTailed-Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ResNet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ResNet · recommended 2×
  2. BERT · recommended 2×
  3. pytorch/pytorch · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. scikit-learn-contrib/imbalanced-learn · recommended 1×
  • CATEGORY QUERY
    How to improve deep learning model performance on datasets with severe class imbalance?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. imbalanced-learn (scikit-learn-contrib/imbalanced-learn)
    4. ResNet
    5. VGG
    6. BERT
    7. GPT
    8. Keras (keras-team/keras)
    9. Albumentations (albumentations-team/albumentations)
    10. TextAttack (TextAttack/TextAttack)

    AI recommended 10 alternatives but never named Vanint/Awesome-LongTailed-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for training neural networks with long-tailed data distributions?
    you: not recommended
    AI recommended (in order):
    1. SMOTE
    2. ADASYN
    3. Random Undersampling
    4. Tomek Links
    5. Edited Nearest Neighbors
    6. Focal Loss
    7. Inverse Frequency Weighting
    8. Effective Number of Samples (ENS) Weighting
    9. PyTorch-LTS
    10. ImageNet
    11. ResNet
    12. EfficientNet
    13. Vision Transformers
    14. BERT
    15. RoBERTa
    16. MAML
    17. Reptile
    18. Prototypical Networks
    19. Random Forest
    20. AdaBoost
    21. XGBoost

    AI recommended 21 alternatives but never named Vanint/Awesome-LongTailed-Learning. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Vanint/Awesome-LongTailed-Learning?
    pass
    AI did not name Vanint/Awesome-LongTailed-Learning — 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 Vanint/Awesome-LongTailed-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Vanint/Awesome-LongTailed-Learning 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 Vanint/Awesome-LongTailed-Learning solve, and who is the primary audience?
    pass
    AI did not name Vanint/Awesome-LongTailed-Learning — 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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Vanint/Awesome-LongTailed-Learning — 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