Deep learning classification models typically train poorly on classes with
small numbers of examples. Motivated by the human ability to solve this task,
models have been developed that transfer knowledge from classes with many
examples to learn classes with few examples. Critically, the majority of these
models transfer knowledge within model feature space. In this work, we
demonstrate that transferring knowledge within classified space is more
effective and efficient. Specifically, by linearly combining strong nearest
neighbor classifiers along with a weak classifier, we are able to compose a
stronger classifier. Uniquely, our model can be implemented on top of any
existing classification model that includes a classifier layer. We showcase the
success of our approach in the task of long-tailed recognition, whereby the
classes with few examples, otherwise known as the "tail" classes, suffer the
most in performance and are the most challenging classes to learn. Using
classifier-level knowledge transfer, we are able to drastically improve - by a
margin as high as 12.6% - the state-of-the-art performance on the "tail"
categories.Comment: Under revie