Title: Few-Shot Learning with Metric-Agnostic Conditional Embeddings,
Abstract: Learning high quality class representations from few examples is a key
problem in metric-learning approaches to few-shot learning. To accomplish this,
we introduce a novel architecture where class representations are conditioned
for each few-shot trial based on a target image. We also deviate from
traditional metric-learning approaches by training a network to perform
comparisons between classes rather than relying on a static metric comparison.
This allows the network to decide what aspects of each class are important for
the comparison at hand. We find that this flexible architecture works well in
practice, achieving state-of-the-art performance on the Caltech-UCSD birds
fine-grained classification task.