Abstract
Humans can quickly learn new visual concepts, perhaps
because they can easily visualize or imagine what novel
objects look like from different views. Incorporating this
ability to hallucinate novel instances of new concepts might
help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present
a novel approach to low-shot learning that uses this idea.
Our approach builds on recent progress in meta-learning
(“learning to learn”) by combining a meta-learner with a
“hallucinator” that produces additional training examples,
and optimizing both models jointly. Our hallucinator can
be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark