  Many recent machine learning tasks focus to develop models that can
generalize to unseen distributions. Domain generalization (DG) has become one
of the key topics in various fields. Several literatures show that DG can be
arbitrarily hard without exploiting target domain information. To address this
issue, test-time adaptive (TTA) methods are proposed. Existing TTA methods
require offline target data or extra sophisticated optimization procedures
during the inference stage. In this work, we adopt Non-Parametric Classifier to
perform the test-time Adaptation (AdaNPC). In particular, we construct a memory
that contains the feature and label pairs from training domains. During
inference, given a test instance, AdaNPC first recalls K closed samples from
the memory to vote for the prediction, and then the test feature and predicted
label are added to the memory. In this way, the sample distribution in the
memory can be gradually changed from the training distribution towards the test
distribution with very little extra computation cost. We theoretically justify
the rationality behind the proposed method. Besides, we test our model on
extensive numerical experiments. AdaNPC significantly outperforms competitive
baselines on various DG benchmarks. In particular, when the adaptation target
is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than
advanced TTA methods. The code is available at
https://github.com/yfzhang114/AdaNPC.
