  Neural architecture search has recently attracted lots of research efforts as
it promises to automate the manual design of neural networks. However, it
requires a large amount of computing resources and in order to alleviate this,
a performance prediction network has been recently proposed that enables
efficient architecture search by forecasting the performance of candidate
architectures, instead of relying on actual model training. The performance
predictor is task-aware taking as input not only the candidate architecture but
also task meta-features and it has been designed to collectively learn from
several tasks. In this work, we introduce a pairwise ranking loss for training
a network able to rank candidate architectures for a new unseen task
conditioning on its task meta-features. We present experimental results,
showing that the ranking network is more effective in architecture search than
the previously proposed performance predictor.
