  Low-shot visual learning---the ability to recognize novel object categories
from very few examples---is a hallmark of human visual intelligence. Existing
machine learning approaches fail to generalize in the same way. To make
progress on this foundational problem, we present a low-shot learning benchmark
on complex images that mimics challenges faced by recognition systems in the
wild. We then propose a) representation regularization techniques, and b)
techniques to hallucinate additional training examples for data-starved
classes. Together, our methods improve the effectiveness of convolutional
networks in low-shot learning, improving the one-shot accuracy on novel classes
by 2.3x on the challenging ImageNet dataset.
