  We consider the problem of selective prediction (also known as reject option)
in deep neural networks, and introduce SelectiveNet, a deep neural architecture
with an integrated reject option. Existing rejection mechanisms are based
mostly on a threshold over the prediction confidence of a pre-trained network.
In contrast, SelectiveNet is trained to optimize both classification (or
regression) and rejection simultaneously, end-to-end. The result is a deep
neural network that is optimized over the covered domain. In our experiments,
we show a consistently improved risk-coverage trade-off over several well-known
classification and regression datasets, thus reaching new state-of-the-art
results for deep selective classification.
