Abstract
In this work we present a novel unsupervised framework
for hard training example mining. The only input to the
method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g.
by pre-trained CNN. Positive examples are distant points
on a single manifold, while negative examples are nearby
points on different manifolds. Both types of examples are
revealed by disagreements between Euclidean and manifold
similarities. The discovered examples can be used in training with any discriminative loss.
The method is applied to unsupervised fine-tuning of pretrained networks for fine-grained classification and particular object retrieval. Our models are on par or are outperforming prior models that are fully or partially supervised