Feature Request: `Implement Threshold-Consistent Margin Loss for Open-World Deep Metric Learning in TF-GNN`
@Vamsi995 is already working on this.
Since Jan 31, 2025.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python, tensorflow
- Domain
- machine-learning
Research direction
No files, tests, or entry points are named. Start by reading the linked paper and locating TF-GNN's existing loss-function and component-integration APIs; done means a TCM operation with configurable margin and temperature, integrated into TF-GNN and accompanied by documentation and examples.
Written by the indexing model from the issue text.
Description
I propose adding the Threshold-Consistent Margin Loss (TCM) function to the TF-GNN library. TCM is a novel loss function specifically designed for open-world deep metric learning, which has shown significant improvements in handling unseen classes and imbalanced data compared to traditional loss functions.
Motivation:
Open-world scenarios: Many real-world applications involve open-world scenarios where new classes can emerge over time. TCM is well-suited for these challenges.
Improved performance: TCM has demonstrated superior performance in terms of accuracy and robustness compared to other loss functions in open-world settings.
Community benefit: Incorporating TCM into TF-GNN will benefit the broader machine learning community by providing a powerful tool for addressing open-world problems.
Implementation details:
Function definition: Implement the TCM loss function as a TensorFlow operation.
Hyperparameters: Allow users to configure TCM hyperparameters (e.g., margin, temperature) to fine-tune the loss.
Integration: Integrate TCM with existing TF-GNN components for seamless usage.
Documentation: Provide clear documentation and examples to guide users in using TCM effectively.
Additional notes:
Consider providing pre-trained models or transfer learning options to accelerate development.
Explore opportunities for optimization and performance improvements.
By incorporating TCM into TF-GNN, we can significantly enhance the library's capabilities for open-world deep metric learning and empower researchers and developers to tackle challenging real-world problems.
- Dominant language
- Python
- Stars
- 1.5k
- Forks
- 206
- Avg merge
- 7h 29m
- Merged PRs (30d)
- 3
Getting set up
- Ships a Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
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.
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