DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations
The episode discusses 'DirMixE,' a paper addressing long-tail recognition where test data distribution is unknown or skewed. The hosts explore how DirMixE uses a Mixture of Experts to handle these unpredictable distributions, achieving high performance and stability on benchmarks like ImageNet and while being efficient enough to fine-tune large foundation models.