  This work generalizes the problem of unsupervised domain generalization to
the case in which no labeled samples are available (completely unsupervised).
We are given unlabeled samples from multiple source domains, and we aim to
learn a shared predictor that assigns examples to semantically related
clusters. Evaluation is done by predicting cluster assignments in previously
unseen domains. Towards this goal, we propose a two-stage training framework:
(1) self-supervised pre-training for extracting domain invariant semantic
features. (2) multi-head cluster prediction with pseudo labels, which rely on
both the feature space and cluster head prediction, further leveraging a novel
prediction-based label smoothing scheme. We demonstrate empirically that our
model is more accurate than baselines that require fine-tuning using samples
from the target domain or some level of supervision. Our code is available at
https://github.com/AmitRozner/domain-generalizable-multiple-domain-clustering.
