Test time adaptation is the process of adapting, in an unsupervised manner, a pre-trained source model to each incoming batch of the test data (i.e., without requiring a substantial portion of the test data to be available, as in traditional domain adaptation) and without access to the source data. Since it works with each batch of test data, it is well-suited for dynamic environments where decisions need to be made as the data is streaming in. Current test time adaptation methods are primarily focused on a single source model. We propose the first completely unsupervised Multi-source Test Time Adaptation (MeTA) framework that handles multiple source models and optimally combines them to adapt to the test data. MeTA has two distinguishing features. First, it efficiently obtains the optimal combination weights to combine the source models to adapt to the test data distribution. Second, it identifies which of the source model parameters to update so that only the model which is most correlated to the target data is adapted, leaving the less correlated ones untouched; this mitigates the issue of "forgetting" the source model parameters by focusing only on the source model that exhibits the strongest correlation with the test batch distribution. Experiments on diverse datasets demonstrate that the combination of multiple source models does at least as well as the best source (with hindsight knowledge), and performance does not degrade as the test data distribution changes over time (robust to forgetting).

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