  We train a deep convolutional neural network to perform identity
classification using a new dataset of public figures annotated with age,
gender, ethnicity and emotion labels, and then fine-tune it for attribute
classification. An optimal sharing pattern of computational resources within
this network is determined by experiment, requiring only 1 G flops to produce
all predictions. Rather than fine-tune by relearning weights in one additional
layer after the penultimate layer of the identity network, we try several
different depths for each attribute. We find that prediction of age and emotion
is improved by fine-tuning from earlier layers onward, presumably because
deeper layers are progressively invariant to non-identity related changes in
the input.
