EGiME: Entropy-Guided Model Evaluation in Few-Shot Learning
arXiv:2610.27029v1 Announce Type: new Abstract: The feature-extraction quality of pre-trained models sets the ceiling of downstream transfer learning, yet few-shot model selection remains difficult: supervised evaluation on scarce labels is unstable and yields fluctuating rankings. We propose Entro…
Read the full story at arXiv cs.LG ↗
Timeline · 1 report
- 2026-10-21 04:00 · arXiv cs.LG
EGiME: Entropy-Guided Model Evaluation in Few-Shot Learning