Beyond Scarcity: When Encoder Adaptation Is Worthwhile
Read the original on arXiv Machine Learning →The paper introduces a controlled evaluation framework to determine when encoder adaptation is beneficial after correcting for class imbalance in long‑tailed recognition tasks. By separating unique support from representation allocation and comparing classifier‑only versus full‑network continuations, the study shows that encoder updates become worthwhile only when representation allocation is highly imbalanced, a transition that persists across various corrections and scales to ImageNet. The framework uses stage‑specific controls, multiple objectives, whole‑distribution metrics, and hierarchical inference to robustly estimate incremental encoder value.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.