Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
ICLR 2024(2024)
摘要
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for
unseen test data. The primary challenge of TTA is limited access to the entire
test dataset during online updates, causing error accumulation. To mitigate it,
TTA methods have utilized the model output's entropy as a confidence metric
that aims to determine which samples have a lower likelihood of causing error.
Through experimental studies, however, we observed the unreliability of entropy
as a confidence metric for TTA under biased scenarios and theoretically
revealed that it stems from the neglect of the influence of latent disentangled
factors of data on predictions. Building upon these findings, we introduce a
novel TTA method named Destroy Your Object (DeYO), which leverages a newly
proposed confidence metric named Pseudo-Label Probability Difference (PLPD).
PLPD quantifies the influence of the shape of an object on prediction by
measuring the difference between predictions before and after applying an
object-destructive transformation. DeYO consists of sample selection and sample
weighting, which employ entropy and PLPD concurrently. For robust adaptation,
DeYO prioritizes samples that dominantly incorporate shape information when
making predictions. Our extensive experiments demonstrate the consistent
superiority of DeYO over baseline methods across various scenarios, including
biased and wild. Project page is publicly available at
https://whitesnowdrop.github.io/DeYO/.
更多查看译文
关键词
Test-time adaptation,Roustness
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要