XtarNet: Learning to Extract Task Adaptive Representation for Incremental Few Shot Learning
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.
ICML