OSDTW: Optimal Shared Depth and Task Weighting for Long-Tailed Recognition
📰 ArXiv cs.AI
Learn to optimize shared depth and task weighting for long-tailed recognition using OSDTW, improving performance on tail classes without degrading head accuracy
Action Steps
- Implement OSDTW to optimize shared depth and task weighting
- Use re-weighting and decoupled training to improve tail performance
- Apply multi-expert methods to enhance representation sharing between head and tail classes
- Configure supervision weighting across class groups to reduce training instability
- Test OSDTW on long-tailed recognition benchmarks to evaluate performance
Who Needs to Know This
Machine learning engineers and researchers working on long-tailed recognition tasks can benefit from this approach to improve model performance and stability
Key Insight
💡 OSDTW optimizes shared depth and task weighting to improve long-tailed recognition performance, reducing the head-tail trade-off
Share This
🚀 Improve long-tailed recognition with OSDTW! Optimize shared depth and task weighting to boost tail performance without degrading head accuracy 📈
Key Takeaways
Learn to optimize shared depth and task weighting for long-tailed recognition using OSDTW, improving performance on tail classes without degrading head accuracy
Full Article
Title: OSDTW: Optimal Shared Depth and Task Weighting for Long-Tailed Recognition
Abstract:
arXiv:2605.24969v1 Announce Type: cross Abstract: Long-tailed recognition suffers from a persistent head--tail trade-off: improving tail performance often degrades head accuracy and can increase training instability. Despite strong empirical results from re-weighting, decoupled training, and multi-expert methods, key design choices about representation sharing between head and tail classes and supervision weighting across class groups remain largely heuristic. In this work, we propose OSDTW, a p
Abstract:
arXiv:2605.24969v1 Announce Type: cross Abstract: Long-tailed recognition suffers from a persistent head--tail trade-off: improving tail performance often degrades head accuracy and can increase training instability. Despite strong empirical results from re-weighting, decoupled training, and multi-expert methods, key design choices about representation sharing between head and tail classes and supervision weighting across class groups remain largely heuristic. In this work, we propose OSDTW, a p
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