  Generalizing deep learning models to unknown target domain distribution with
low latency has motivated research into test-time training/adaptation
(TTT/TTA). Existing approaches often focus on improving test-time training
performance under well-curated target domain data. As figured out in this work,
many state-of-the-art methods fail to maintain the performance when the target
domain is contaminated with strong out-of-distribution (OOD) data, a.k.a.
open-world test-time training (OWTTT). The failure is mainly due to the
inability to distinguish strong OOD samples from regular weak OOD samples. To
improve the robustness of OWTTT we first develop an adaptive strong OOD pruning
which improves the efficacy of the self-training TTT method. We further propose
a way to dynamically expand the prototypes to represent strong OOD samples for
an improved weak/strong OOD data separation. Finally, we regularize
self-training with distribution alignment and the combination yields the
state-of-the-art performance on 5 OWTTT benchmarks. The code is available at
https://github.com/Yushu-Li/OWTTT.
