  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.
