  We explore the problem of Incremental Generalized Category Discovery (IGCD).
This is a challenging category incremental learning setting where the goal is
to develop models that can correctly categorize images from previously seen
categories, in addition to discovering novel ones. Learning is performed over a
series of time steps where the model obtains new labeled and unlabeled data,
and discards old data, at each iteration. The difficulty of the problem is
compounded in our generalized setting as the unlabeled data can contain images
from categories that may or may not have been observed before. We present a new
method for IGCD which combines non-parametric categorization with efficient
image sampling to mitigate catastrophic forgetting. To quantify performance, we
propose a new benchmark dataset named iNatIGCD that is motivated by a
real-world fine-grained visual categorization task. In our experiments we
outperform existing related methods
