  Deep learning methods have typically been trained on large datasets in which
many training examples are available. However, many real-world product datasets
have only a small number of images available for each product. We explore the
use of deep learning methods for recognizing object instances when we have only
a single training example per class. We show that feedforward neural networks
outperform state-of-the-art methods for recognizing objects from novel
viewpoints even when trained from just a single image per object. To further
improve our performance on this task, we propose to take advantage of a
supplementary dataset in which we observe a separate set of objects from
multiple viewpoints. We introduce a new approach for training deep learning
methods for instance recognition with limited training data, in which we use an
auxiliary multi-view dataset to train our network to be robust to viewpoint
changes. We find that this approach leads to a more robust classifier for
recognizing objects from novel viewpoints, outperforming previous
state-of-the-art approaches including keypoint-matching, template-based
techniques, and sparse coding.
