BriefGPT.xyz
May, 2023
标签平滑是针对模型错配的鲁棒化方法
Label Smoothing is Robustification against Model Misspecification
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Ryoya Yamasaki, Toshiyuki Tanaka
TL;DR
本文研究了标签平滑(label smoothing)技术的两个修改:损失函数及概率估计,提出了一种改进版的modified LSLR,并通过理论和实验分别证明了其具有更高的鲁棒性和更糟糕的概率估计性能。
Abstract
label smoothing
(LS) adopts smoothed targets in
classification
tasks. For example, in binary
classification
, instead of the one-hot target
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