论文标题
返回来源：扩散驱动的测试时间适应
Back to the Source: Diffusion-Driven Test-Time Adaptation
论文作者
论文摘要
测试时间适应利用测试输入，以提高对源数据进行训练的模型的准确性，该模型在转移的目标数据上进行了测试。现有方法通过（重新）对每个目标域进行培训来更新源模型。虽然有效，但重新训练对数据的数量和顺序和优化的超参数敏感。而是通过使用生成扩散模型将所有测试输入投影到源域，我们将更新目标数据。我们的扩散驱动的适应方法DDA共享其在所有领域的分类和生成模型。两种模型均在源域上训练，然后在测试过程中固定。我们通过图像指导加强扩散，自动决定适应多少。 DDA的输入适应比在Imagenet-C基准上的各种损坏，架构和数据制度中的先前模型适应方法更为强大。借助其输入更新，DDA成功了，在小批次中，模型适应性降低了太少的数据，以不均匀顺序的依赖数据或具有多个损坏的混合数据。
Test-time adaptation harnesses test inputs to improve the accuracy of a model trained on source data when tested on shifted target data. Existing methods update the source model by (re-)training on each target domain. While effective, re-training is sensitive to the amount and order of the data and the hyperparameters for optimization. We instead update the target data, by projecting all test inputs toward the source domain with a generative diffusion model. Our diffusion-driven adaptation method, DDA, shares its models for classification and generation across all domains. Both models are trained on the source domain, then fixed during testing. We augment diffusion with image guidance and self-ensembling to automatically decide how much to adapt. Input adaptation by DDA is more robust than prior model adaptation approaches across a variety of corruptions, architectures, and data regimes on the ImageNet-C benchmark. With its input-wise updates, DDA succeeds where model adaptation degrades on too little data in small batches, dependent data in non-uniform order, or mixed data with multiple corruptions.
