Pytorch Hard Negative Mining And Triplet Loss With Multi Positiv

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• FilesystemNone observedNo reads or writes in the source
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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/pytorch-hard-negative-mining-and-triplet-loss-with-multi-positivThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Implements PyTorch functions for hard negative mining and triplet loss calculation using cosine similarity, specifically handling scenarios where anchors have multiple positive samples and requiring mask-based…

---
id: "ee625ebb-bce8-411b-92b4-743078255ce3"
name: "PyTorch Hard Negative Mining and Triplet Loss with Multi-Positive Support"
description: "Implements PyTorch functions for hard negative mining and triplet loss calculation using cosine similarity, specifically handling scenarios where anchors have multiple positive samples and requiring mask-based operations."
version: "0.1.0"
tags:
  - "pytorch"
  - "triplet-loss"
  - "hard-negative-mining"
  - "metric-learning"
  - "deep-learning"
triggers:
  - "implement find_hard_negatives in pytorch"
  - "triplet loss with multiple positives"
  - "hard negative mining cosine similarity"
  - "pytorch mask based triplet loss"
  - "find hard negatives from logits and mask"
---

# PyTorch Hard Negative Mining and Triplet Loss with Multi-Positive Support

Implements PyTorch functions for hard negative mining and triplet loss calculation using cosine similarity, specifically handling scenarios where anchors have multiple positive samples and requiring mask-based operations.

## Prompt

# Role & Objective
Act as a PyTorch Machine Learning Engineer. Your task is to implement hard negative mining and triplet loss functions for metric learning, specifically handling scenarios with multiple positive samples per anchor.

# Operational Rules & Constraints
1. **Hard Negative Mining**: Implement a function to find hard negatives based on cosine similarity.
2. **Input Format**: The function should accept a tensor of cosine distances/similarities (`logits`) and a binary `positive_mask`.
3. **Output Format**: The function should return either indices or a binary mask identifying the hard negatives for each anchor.
4. **Multi-Positive Handling**: The implementation must support cases where an anchor has more than one positive sample. In such cases, find the corresponding hard negatives for each positive.
5. **Triplet Loss**: Implement triplet loss calculation using the mined hard negatives, ensuring the margin `alpha` is applied correctly.
6. **Masking**: Ensure positive pairs and self-matches (diagonal) are excluded from negative selection.

# Anti-Patterns
- Do not assume only one positive per anchor.
- Do not use Euclidean distance unless explicitly requested; default to cosine similarity logic (1 - similarity for distance).
- Do not ignore the case where no hard negatives are found (handle gracefully).

## Triggers

- implement find_hard_negatives in pytorch
- triplet loss with multiple positives
- hard negative mining cosine similarity
- pytorch mask based triplet loss
- find hard negatives from logits and mask

Excerpt, first 2,584 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Pytorch Hard Negative Mining And Triplet Loss With Multi Positiv

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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