超越熵：风格迁移引导的单图像持续测试时自适应
Beyond Entropy: Style Transfer Guided Single Image Continual Test-Time Adaptation
AI总结：
本文提出BESTTA方法，通过风格迁移引导单图像持续测试时自适应，并设计BeIN归一化层与风格引导损失，以极低内存开销在语义分割和图像分类任务上超越现有方法。
摘要：
持续测试时自适应（cTTA）方法旨在促进模型在计算资源受限的动态变化现实环境中进行持续自适应。由于这一固有限制，现有方法无法同时实现准确性和效率。具体而言，在使用单张图像时，批量归一化层和熵损失引起的不稳定性会显著破坏许多现有方法在现实cTTA场景中的稳定性。为克服这些挑战，我们提出了BESTTA，一种由风格迁移引导的新型单图像持续测试时自适应方法，该方法通过将输入图像的风格迁移到源风格，实现对目标环境的稳定且高效的自适应。为实现所提出的方法，我们设计了BeIN，一种简单而强大的归一化方法，以及风格引导损失。我们证明，BESTTA在语义分割和图像分类任务上仅利用单张图像即可有效适应持续变化的目标环境。值得注意的是，尽管仅在BeIN层中训练两个参数且消耗最少的内存，BESTTA在性能上仍优于现有的最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
