  Learning novel concepts while preserving prior knowledge is a long-standing
challenge in machine learning. The challenge gets greater when a novel task is
given with only a few labeled examples, a problem known as incremental few-shot
learning. We propose XtarNet, which learns to extract task-adaptive
representation (TAR) for facilitating incremental few-shot learning. The method
utilizes a backbone network pretrained on a set of base categories while also
employing additional modules that are meta-trained across episodes. Given a new
task, the novel feature extracted from the meta-trained modules is mixed with
the base feature obtained from the pretrained model. The process of combining
two different features provides TAR and is also controlled by meta-trained
modules. The TAR contains effective information for classifying both novel and
base categories. The base and novel classifiers quickly adapt to a given task
by utilizing the TAR. Experiments on standard image datasets indicate that
XtarNet achieves state-of-the-art incremental few-shot learning performance.
The concept of TAR can also be used in conjunction with existing incremental
few-shot learning methods; extensive simulation results in fact show that
applying TAR enhances the known methods significantly.
