  We propose a learning algorithm capable of learning from label proportions
instead of direct data labels. In this scenario, our data are arranged into
various bags of a certain size, and only the proportions of each label within a
given bag are known. This is a common situation in cases where per-data
labeling is lengthy, but a more general label is easily accessible. Several
approaches have been proposed to learn in this setting with linear models in
the multiclass setting, or with nonlinear models in the binary classification
setting. Here we investigate the more general nonlinear multiclass setting, and
compare two differentiable loss functions to train end-to-end deep neural
networks from bags with label proportions. We illustrate the relevance of our
methods on an image classification benchmark, and demonstrate the possibility
to learn accurate image classifiers from bags of images.
