  We present a simple and effective way to estimate the batch-norm statistics
during test time, to fast adapt a source model to target test samples. Known as
Test-Time Adaptation, most prior works studying this task follow two
assumptions in their evaluation where (1) test samples come together as a large
batch, and (2) all from a single test distribution. However, in practice, these
two assumptions may not stand, the reasons for which we propose two new
evaluation settings where batch sizes are arbitrary and multiple distributions
are considered. Unlike the previous methods that require a large batch of
single distribution during test time to calculate stable batch-norm statistics,
our method avoid any dependency on large online batches and is able to estimate
accurate batch-norm statistics with a single sample. The proposed method
significantly outperforms the State-Of-The-Art in the newly proposed settings
in Test-Time Adaptation Task, and also demonstrates improvements in various
other settings such as Source-Free Unsupervised Domain Adaptation and Zero-Shot
Classification.
