  Authentication is the task of confirming the matching relationship between a
data instance and a given identity. Typical examples of authentication problems
include face recognition and person re-identification. Data-driven
authentication could be affected by undesired biases, i.e., the models are
often trained in one domain (e.g., for people wearing spring outfits) while
applied in other domains (e.g., they change the clothes to summer outfits).
Previous works have made efforts to eliminate domain-difference. They typically
assume domain annotations are provided, and all the domains share classes.
However, for authentication, there could be a large number of domains shared by
different identities/classes, and it is impossible to annotate these domains
exhaustively. It could make domain-difference challenging to model and
eliminate. In this paper, we propose a domain-agnostic method that eliminates
domain-difference without domain labels. We alternately perform latent domain
discovery and domain-difference elimination until our model no longer detects
domain-difference. In our approach, the latent domains are discovered by
learning the heterogeneous predictive relationships between inputs and outputs.
Then domain-difference is eliminated in both class-dependent and
class-independent spaces to improve robustness of elimination. We further
extend our method to a meta-learning framework to pursue more thorough
domain-difference elimination. Comprehensive empirical evaluation results are
provided to demonstrate the effectiveness and superiority of our proposed
method.
