TIGER-AI-Lab / TIGER-AI-Lab/EditReward
Data Annotation Format Issue
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- Dominant language
- Python
- Stars
- 160
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
If the model is optimized using the loss defined as the negative log-likelihood of the ground-truth preference from the paper, does this mean we only leverage the relative quality between the two edited images without using their exact numerical scores at all?
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, test, or entry point is named. Start by reading the paper's loss definition and the repository's annotation and training materials to identify whether supervision uses only pairwise preference or also numerical scores. Done means documenting the annotation format and the role of exact scores clearly enough to resolve the question.
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Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100