  Universal Domain Adaptation aims to transfer the knowledge between the
datasets by handling two shifts: domain-shift and category-shift. The main
challenge is correctly distinguishing the unknown target samples while adapting
the distribution of known class knowledge from source to target. Most existing
methods approach this problem by first training the target adapted known
classifier and then relying on the single threshold to distinguish unknown
target samples. However, this simple threshold-based approach prevents the
model from considering the underlying complexities existing between the known
and unknown samples in the high-dimensional feature space. In this paper, we
propose a new approach in which we use two sets of feature points, namely dual
Classifiers for Prototypes and Reciprocals (CPR). Our key idea is to associate
each prototype with corresponding known class features while pushing the
reciprocals apart from these prototypes to locate them in the potential unknown
feature space. The target samples are then classified as unknown if they fall
near any reciprocals at test time. To successfully train our framework, we
collect the partial, confident target samples that are classified as known or
unknown through on our proposed multi-criteria selection. We then additionally
apply the entropy loss regularization to them. For further adaptation, we also
apply standard consistency regularization that matches the predictions of two
different views of the input to make more compact target feature space. We
evaluate our proposal, CPR, on three standard benchmarks and achieve comparable
or new state-of-the-art results. We also provide extensive ablation experiments
to verify our main design choices in our framework.
