Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture- and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance.

翻译：测试时训练（TTT）是一种通过将训练好的模型适应测试时出现的分布偏移来处理分布外（OOD）数据的方法。我们提出通过激活匹配（ActMAD）实现这种适应：我们分析模型的激活值，并将OOD测试数据的激活统计量与训练数据的激活统计量对齐。与现有方法（即仅对特征提取器最终层中整个通道的分布进行建模）不同，我们对网络中多个层的每个特征分布进行建模。这带来了更细粒度的监督，使ActMAD在CIFAR-100C和ImageNet-C上达到了最先进的性能。ActMAD还具有架构和任务无关性，使其能够超越图像分类任务：在基于KITTI训练的目标检测器评估KITTI-Fog数据集时，我们的方法相较先前方法提升了15.4%的性能。实验表明，ActMAD可应用于现实场景中的在线适应，仅需少量数据即可达到完整性能。