Test-time Adaptation Meets Image Enhancement: Improving Accuracy via
  Uncertainty-aware Logit Switching
Shohei Enomoto Naoya Hasegawa Kazuki Adachi Taku Sasaki Shin'ya Yamaguchi Satoshi Suzuki Takeharu Eda
Deep neural networks have achieved remarkable success in a variety of
computer vision applications. However, there is a problem of degrading accuracy
when the data distribution shifts between training and testing. As a solution
of this problem, Test-time Adaptation~(TTA) has been well studied because of
its practicality. Although TTA methods increase accuracy under distribution
shift by updating the model at test time, using high-uncertainty predictions is
known to degrade accuracy. Since the input image is the root of the
distribution shift, we incorporate a new perspective on enhancing the input
image into TTA methods to reduce the prediction's uncertainty. We hypothesize
that enhancing the input image reduces prediction's uncertainty and increase
the accuracy of TTA methods. On the basis of our hypothesis, we propose a novel
method: Test-time Enhancer and Classifier Adaptation~(TECA). In TECA, the
classification model is combined with the image enhancement model that
transforms input images into recognition-friendly ones, and these models are
updated by existing TTA methods. Furthermore, we found that the prediction from
the enhanced image does not always have lower uncertainty than the prediction
from the original image. Thus, we propose logit switching, which compares the
uncertainty measure of these predictions and outputs the lower one. In our
experiments, we evaluate TECA with various TTA methods and show that TECA
reduces prediction's uncertainty and increases accuracy of TTA methods despite
having no hyperparameters and little parameter overhead.