AutoGrow automatically discovers optimal depth in DNNs.
problem Designing optimal depth in deep neural networks is difficult and time-consuming.
method AutoGrow grows new layers in a seed architecture if it improves accuracy; stops if no improvement. Robust policies generalize to different architectures and datasets.
result AutoGrow discovers near-optimal depth on various datasets, improving accuracy-computation trade-off in ResNets.