通过特征增强提升测试时自适应性能，解决数据少的难题
Feature Augmentation based Test-Time Adaptation
- 用归一化扰动增强输入特征，扩大可用数据量
- 在ImageNet-C和Office-Home上显著提升模型泛化能力
- 无需改动模型结构，适配多种主流模型
测试时自适应（TTA）使模型能在不访问源数据的情况下适应未知领域。由于实际环境限制，TTA可用的数据量本就有限，而现有方法还通过过滤不可靠输入进一步缩小有效数据规模，制约了适应能力。为此，我们提出特征增强型测试时自适应（FATA），一种通过特征增强充分利用有限输入数据的简单方法。FATA采用归一化扰动生成增强特征，并使用FATA损失函数使原始特征与增强特征的输出保持一致。该方法模型无关，可无缝集成到现有模型中且不改变架构。我们在ImageNet-C和Office-Home多个模型和场景下验证了FATA的有效性，证明其在多样真实场景中的优越性。
原文摘要 · Abstract (English)
Test-time adaptation (TTA) allows a model to be adapted to an unseen domain without accessing the source data. Due to the nature of practical environments, TTA has a limited amount of data for adaptation. Recent TTA methods further restrict this by filtering input data for reliability, making the effective data size even smaller and limiting adaptation potential. To address this issue, We propose Feature Augmentation based Test-time Adaptation (FATA), a simple method that fully utilizes the limited amount of input data through feature augmentation. FATA employs Normalization Perturbation to augment features and adapts the model using the FATA loss, which makes the outputs of the augmented and original features similar. FATA is model-agnostic and can be seamlessly integrated into existing models without altering the model architecture. We demonstrate the effectiveness of FATA on various models and scenarios on ImageNet-C and Office-Home, validating its superiority in diverse real-world conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。