  Few-shot class incremental learning -- the problem of updating a trained
classifier to discriminate among an expanded set of classes with limited
labeled data -- is a key challenge for machine learning systems deployed in
non-stationary environments. Existing approaches to the problem rely on complex
model architectures and training procedures that are difficult to tune and
re-use. In this paper, we present an extremely simple approach that enables the
use of ordinary logistic regression classifiers for few-shot incremental
learning. The key to this approach is a new family of subspace regularization
schemes that encourage weight vectors for new classes to lie close to the
subspace spanned by the weights of existing classes. When combined with
pretrained convolutional feature extractors, logistic regression models trained
with subspace regularization outperform specialized, state-of-the-art
approaches to few-shot incremental image classification by up to 22% on the
miniImageNet dataset. Because of its simplicity, subspace regularization can be
straightforwardly extended to incorporate additional background information
about the new classes (including class names and descriptions specified in
natural language); these further improve accuracy by up to 2%. Our results show
that simple geometric regularization of class representations offers an
effective tool for continual learning.
