  Supervised learning datasets often have privileged information, in the form
of features which are available at training time but are not available at test
time e.g. the ID of the annotator that provided the label. We argue that
privileged information is useful for explaining away label noise, thereby
reducing the harmful impact of noisy labels. We develop a simple and efficient
method for supervised learning with neural networks: it transfers via weight
sharing the knowledge learned with privileged information and approximately
marginalizes over privileged information at test time. Our method, TRAM
(TRansfer and Marginalize), has minimal training time overhead and has the same
test-time cost as not using privileged information. TRAM performs strongly on
CIFAR-10H, ImageNet and Civil Comments benchmarks.
