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DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Vartiations
arXiv – CS AI|Zhiyong Yang, Qianqian Xu, Sicong Li, Zitai Wang, Xiaochun Cao, Qingming Huang||1 views
🤖AI Summary
Researchers introduce DirMixE, a new machine learning approach for handling test-agnostic long-tail recognition problems where test data distributions are unknown and imbalanced. The method uses a hierarchical Mixture-of-Expert strategy with Dirichlet meta-distributions and includes a Latent Skill Finetuning framework for efficient parameter tuning of foundation models.
Key Takeaways
- →DirMixE addresses long-tail recognition by capturing both global and local variations in unknown test distributions through hierarchical decomposition.
- →The approach assigns experts to different Dirichlet meta-distributions, improving upon traditional Mixture-of-Expert methods that only handle global variations.
- →A new Latent Skill Finetuning framework enables parameter-efficient fine-tuning of foundation models using LoRA and Adapter implementations.
- →Theoretical analysis provides upper bounds on generalization error and proves that variance-based regularization helps tighten these bounds.
- →Experimental validation on multiple datasets including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist demonstrates the method's effectiveness.
#machine-learning#long-tail-recognition#mixture-of-experts#foundation-models#parameter-efficient-finetuning#computer-vision#research#arxiv
Read Original →via arXiv – CS AI
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