Domain Agnostic Learning for Unbiased Authentication
release_j4woedrc5bbpzfyqfbv6f6kzc4
by
Jian Liang, Yuren Cao, Shuang Li, Bing Bai, Hao Li, Fei Wang, Kun Bai
2020
Abstract
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 components. Comprehensive empirical evaluation results are
provided to demonstrate the effectiveness and superiority of our proposed
method.
In text/plain
format
Archived Content
There are no accessible files associated with this release. You could check other releases for this work for an accessible version.
Know of a fulltext copy of on the public web? Submit a URL and we will archive it
2010.05250v1
access all versions, variants, and formats of this works (eg, pre-prints)