RIDE+distill

Reported on 5 benchmarks across 5 tasks · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology3 results

Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2020-10-05
50.9
best: 10.9 (LPT)
SOTA
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts arXiv:2010.01809
Long-tail LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2020-10-05
50.9
best: 10.9 (LPT)
SOTA
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts arXiv:2010.01809
Generalized Few-Shot LearningonCIFAR-100-LT (ρ=100)
Error Rate· 2020-10-05
50.9
best: 10.9 (LPT)
SOTA
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts arXiv:2010.01809

Computer Vision2 results

Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2020-10-05
50.9
best: 10.9 (LPT)
SOTA
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts arXiv:2010.01809
Few-Shot Image ClassificationonCIFAR-100-LT (ρ=100)
Error Rate· 2020-10-05
50.9
best: 10.9 (LPT)
SOTA
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts arXiv:2010.01809