  Unsupervised learning of identity-discriminative visual feature is appealing
in real-world tasks where manual labelling is costly. However, the images of an
identity can be visually discrepant when images are taken under different
states, e.g. different camera views and poses. This visual discrepancy leads to
great difficulty in unsupervised discriminative learning. Fortunately, in
real-world tasks we could often know the states without human annotation, e.g.
we can easily have the camera view labels in person re-identification and
facial pose labels in face recognition. In this work we propose utilizing the
state information as weak supervision to address the visual discrepancy caused
by different states. We formulate a simple pseudo label model and utilize the
state information in an attempt to refine the assigned pseudo labels by the
weakly supervised decision boundary rectification and weakly supervised feature
drift regularization. We evaluate our model on unsupervised person
re-identification and pose-invariant face recognition. Despite the simplicity
of our method, it could outperform the state-of-the-art results on Duke-reID,
MultiPIE and CFP datasets with a standard ResNet-50 backbone. We also find our
model could perform comparably with the standard supervised fine-tuning results
on the three datasets. Code is available at
https://github.com/KovenYu/state-information
