  This paper proposes a method to ease the unsupervised learning of object
landmark detectors. Similarly to previous methods, our approach is fully
unsupervised in a sense that it does not require or make any use of annotated
landmarks for the target object category. Contrary to previous works, we do
however assume that a landmark detector, which has already learned a structured
representation for a given object category in a fully supervised manner, is
available. Under this setting, our main idea boils down to adapting the given
pre-trained network to the target object categories in a fully unsupervised
manner. To this end, our method uses the pre-trained network as a core which
remains frozen and does not get updated during training, and learns, in an
unsupervised manner, only a projection matrix to perform the adaptation to the
target categories. By building upon an existing structured representation
learned in a supervised manner, the optimization problem solved by our method
is much more constrained with significantly less parameters to learn which
seems to be important for the case of unsupervised learning. We show that our
method surpasses fully unsupervised techniques trained from scratch as well as
a strong baseline based on fine-tuning, and produces state-of-the-art results
on several datasets. Code can be found at
https://github.com/ESanchezLozano/SAIC-Unsupervised-landmark-detection-NeurIPS2019 .
