  Conventional deep learning classifiers are static in the sense that they are
trained on a predefined set of classes and learning to classify a novel class
typically requires re-training. In this work, we address the problem of
Low-Shot network expansion learning. We introduce a learning framework which
enables expanding a pre-trained (base) deep network to classify novel classes
when the number of examples for the novel classes is particularly small. We
present a simple yet powerful hard distillation method where the base network
is augmented with additional weights to classify the novel classes, while
keeping the weights of the base network unchanged. We show that since only a
small number of weights needs to be trained, the hard distillation excels in
low-shot training scenarios. Furthermore, hard distillation avoids detriment to
classification performance on the base classes. Finally, we show that low-shot
network expansion can be done with a very small memory footprint by using a
compact generative model of the base classes training data with only a
negligible degradation relative to learning with the full training set.

Machine Learning;Machine Learning