  The problem of long-tailed recognition, where the number of examples per
class is highly unbalanced, is considered. It is hypothesized that the well
known tendency of standard classifier training to overfit to popular classes
can be exploited for effective transfer learning. Rather than eliminating this
overfitting, e.g. by adopting popular class-balanced sampling methods, the
learning algorithm should instead leverage this overfitting to transfer
geometric information from popular to low-shot classes. A new classifier
architecture, GistNet, is proposed to support this goal, using constellations
of classifier parameters to encode the class geometry. A new learning algorithm
is then proposed for GeometrIc Structure Transfer (GIST), with resort to a
combination of loss functions that combine class-balanced and random sampling
to guarantee that, while overfitting to the popular classes is restricted to
geometric parameters, it is leveraged to transfer class geometry from popular
to few-shot classes. This enables better generalization for few-shot classes
without the need for the manual specification of class weights, or even the
explicit grouping of classes into different types. Experiments on two popular
long-tailed recognition datasets show that GistNet outperforms existing
solutions to this problem.
