REPOGEO REPORT · LITE
oneTaken/awesome_deep_learning_interpretability
Default branch master · commit 998e2c37 · scanned 6/5/2026, 9:08:17 PM
GitHub: 767 stars · 123 forks
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 oneTaken/awesome_deep_learning_interpretability, 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.
- highreadme#1Reposition the README's opening sentence to clarify its nature as an 'awesome list'
Why:
CURRENT# awesome_deep_learning_interpretability 深度学习近年来关于模型解释性的相关论文。
COPY-PASTE FIX# awesome_deep_learning_interpretability 一个精选的深度学习可解释性（XAI）高引用/顶会论文与代码的Awesome List。
- mediumhomepage#2Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXSet the homepage URL to `https://github.com/oneTaken/awesome_deep_learning_interpretability`
- lowtopics#3Add 'xai' and 'explainable-ai' to the repository topics
Why:
CURRENTawesome, awesome-list, chainer, computer-vision, cvpr, deep-learning, eccv, iccv, iclr, icml, interpretability, keras, matlab, neural-network, neurips, nlp, papers, pytorch, tensorflow, torch
COPY-PASTE FIXawesome, awesome-list, chainer, computer-vision, cvpr, deep-learning, eccv, explainable-ai, iccv, iclr, icml, interpretability, keras, matlab, neural-network, neurips, nlp, papers, pytorch, tensorflow, torch, xai
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.
- arXiv.org · recommended 1×
- Google Scholar · recommended 1×
- SHAP · recommended 1×
- LIME · recommended 1×
- Grad-CAM · recommended 1×
- CATEGORY QUERYWhere can I find recent research papers on explaining neural network behavior?you: not recommendedAI recommended (in order):
- arXiv.org
- Google Scholar
- SHAP
- LIME
- Grad-CAM
- Integrated Gradients
- NeurIPS
- ICML
- CVPR
- ICLR
- AAAI
- Journal of Machine Learning Research (JMLR)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- Nature Machine Intelligence
- GitHub
AI recommended 16 alternatives but never named oneTaken/awesome_deep_learning_interpretability. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the best methods and code examples for interpreting deep learning models?you: not recommendedAI recommended (in order):
- shap
- lime
- tf-keras-vis
- pytorch-gradcam
- Captum
- Hugging Face Transformers
- bertviz
- Lucid
- eli5
- scikit-learn
AI recommended 10 alternatives but never named oneTaken/awesome_deep_learning_interpretability. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 oneTaken/awesome_deep_learning_interpretability?passAI did not name oneTaken/awesome_deep_learning_interpretability — 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 oneTaken/awesome_deep_learning_interpretability in production, what risks or prerequisites should they evaluate first?passAI named oneTaken/awesome_deep_learning_interpretability 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 oneTaken/awesome_deep_learning_interpretability solve, and who is the primary audience?passAI did not name oneTaken/awesome_deep_learning_interpretability — 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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oneTaken/awesome_deep_learning_interpretability — 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