  In the context of Multi Instance Learning, we analyze the Single Instance
(SI) learning objective. We show that when the data is unbalanced and the
family of classifiers is sufficiently rich, the SI method is a useful learning
algorithm. In particular, we show that larger data imbalance, a quality that is
typically perceived as negative, in fact implies a better resilience of the
algorithm to the statistical dependencies of the objects in bags. In addition,
our results shed new light on some known issues with the SI method in the
setting of linear classifiers, and we show that these issues are significantly
less likely to occur in the setting of neural networks. We demonstrate our
results on a synthetic dataset, and on the COCO dataset for the problem of
patch classification with weak image level labels derived from captions.
