  Over the last couple of years few-shot learning (FSL) has attracted great
attention towards minimizing the dependency on labeled training examples. An
inherent difficulty in FSL is the handling of ambiguities resulting from having
too few training samples per class. To tackle this fundamental challenge in
FSL, we aim to train meta-learner models that can leverage prior semantic
knowledge about novel classes to guide the classifier synthesis process. In
particular, we propose semantically-conditioned feature attention and sample
attention mechanisms that estimate the importance of representation dimensions
and training instances. We also study the problem of sample noise in FSL,
towards the utilization of meta-learners in more realistic and imperfect
settings. Our experimental results demonstrate the effectiveness of the
proposed semantic FSL model with and without sample noise.
