Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time
  Adaptation
Younggeol Cho Youngrae Kim Dongman Lee
Continual test-time adaptation (cTTA) methods are designed to facilitate the
continual adaptation of models to dynamically changing real-world environments
where computational resources are limited. Due to this inherent limitation,
existing approaches fail to simultaneously achieve accuracy and efficiency. In
detail, when using a single image, the instability caused by batch
normalization layers and entropy loss significantly destabilizes many existing
methods in real-world cTTA scenarios. To overcome these challenges, we present
BESTTA, a novel single image continual test-time adaptation method guided by
style transfer, which enables stable and efficient adaptation to the target
environment by transferring the style of the input image to the source style.
To implement the proposed method, we devise BeIN, a simple yet powerful
normalization method, along with the style-guided losses. We demonstrate that
BESTTA effectively adapts to the continually changing target environment,
leveraging only a single image on both semantic segmentation and image
classification tasks. Remarkably, despite training only two parameters in a
BeIN layer consuming the least memory, BESTTA outperforms existing
state-of-the-art methods in terms of performance.