  Test-time adaptation (TTA) is an emerging paradigm that addresses
distributional shifts between training and testing phases without additional
data acquisition or labeling cost; only unlabeled test data streams are used
for continual model adaptation. Previous TTA schemes assume that the test
samples are independent and identically distributed (i.i.d.), even though they
are often temporally correlated (non-i.i.d.) in application scenarios, e.g.,
autonomous driving. We discover that most existing TTA methods fail
dramatically under such scenarios. Motivated by this, we present a new
test-time adaptation scheme that is robust against non-i.i.d. test data
streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization
(IABN) that corrects normalization for out-of-distribution samples, and (b)
Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i.d. data stream
from non-i.i.d. stream in a class-balanced manner. Our evaluation with various
datasets, including real-world non-i.i.d. streams, demonstrates that the
proposed robust TTA not only outperforms state-of-the-art TTA algorithms in the
non-i.i.d. setting, but also achieves comparable performance to those
algorithms under the i.i.d. assumption. Code is available at
https://github.com/TaesikGong/NOTE.
