tamlhp/awesome-machine-unlearning

• highreadme#1

  Reposition README opening to clarify repo's nature as a resource collection

  原因：

  当前

  A collection of academic articles, published methodology, and datasets on the subject of **machine unlearning**.

  复制粘贴的修复

  复制

  This repository, **Awesome Machine Unlearning**, is a comprehensive and actively maintained collection of academic articles, published methodologies, and datasets on the subject of machine unlearning. It serves as a living companion to our survey paper, 'A Survey of Machine Unlearning' (Nguyen et al., 2025), providing an organized and sortable resource for researchers, practitioners, and students.

  待办完成忽略
• mediumreadme#2

  Add a 'Scope' or 'What This Repository Is (and Is Not)' section to the README

  原因：

  复制粘贴的修复

  复制

  **What This Repository Is (and Is Not)**
  This repository is a curated list of resources for machine unlearning. It is *not* an implementation library, a software framework, or a tool for performing machine unlearning. Instead, it aims to be a central hub for discovering research papers, datasets, and methodologies in the field.

  待办完成忽略
• lowreadme#3

  Add a FAQ section to the README to address common misconceptions

  原因：

  复制粘贴的修复

  复制

  **FAQ**
  *   **Is this repository a software library or tool?**
      No, `tamlhp/awesome-machine-unlearning` is a curated list of academic resources, not an executable software package.
  *   **Is this repository a single research paper?**
      No, this repository is a comprehensive collection of resources related to machine unlearning. While it serves as a living companion to our survey paper, it is distinct from a single publication.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to effectively remove specific user data from trained machine learning models?

  你：未被推荐
  AI 推荐顺序：

  1. influence-functions
  2. pytorch-unlearning
  3. tensorflow-unlearning
  4. PyTorch
  5. TensorFlow

  AI 推荐了 5 个替代方案，却始终没点名 tamlhp/awesome-machine-unlearning。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Where can I find a comprehensive survey of methods for machine unlearning techniques?

  你：未被推荐
  AI 推荐顺序：

  1. Machine Unlearning: A Survey (Cao et al., 2023)
  2. A Survey on Machine Unlearning: Taxonomies, Applications, and Challenges (Gong et al., 2023)
  3. Machine Unlearning: A Survey (Xu et al., 2023)
  4. Towards Machine Unlearning: A Survey (Bourtoule et al., 2021)
  5. The Right to Be Forgotten in Machine Learning: An Overview (Gupta et al., 2021)
  6. A Survey of Data Deletion in Machine Learning (Izzo et al., 2021)

  AI 推荐了 6 个替代方案，却始终没点名 tamlhp/awesome-machine-unlearning。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of tamlhp/awesome-machine-unlearning?

  pass
  ⚠AI 未点名 tamlhp/awesome-machine-unlearning —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts tamlhp/awesome-machine-unlearning in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 tamlhp/awesome-machine-unlearning

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo tamlhp/awesome-machine-unlearning solve, and who is the primary audience?

  pass
  ⚠AI 未点名 tamlhp/awesome-machine-unlearning —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？