  This paper develops a new EiHi net to solve the out-of-distribution (OoD)
generalization problem in deep learning. EiHi net is a model learning paradigm
that can be blessed on any visual backbone. This paradigm can change the
previous learning method of the deep model, namely find out correlations
between inductive sample features and corresponding categories, which suffers
from pseudo correlations between indecisive features and labels. We fuse SimCLR
and VIC-Reg via explicitly and dynamically establishing the original - positive
- negative sample pair as a minimal learning element, the deep model
iteratively establishes a relationship close to the causal one between features
and labels, while suppressing pseudo correlations. To further validate the
proposed model, and strengthen the established causal relationships, we develop
a human-in-the-loop strategy, with few guidance samples, to prune the
representation space directly. Finally, it is shown that the developed EiHi net
makes significant improvements in the most difficult and typical OoD dataset
Nico, compared with the current SOTA results, without any domain ($e.g.$
background, irrelevant features) information.
