  How to discover and evaluate the true strength of models quickly and
accurately is one of the key challenges in Neural Architecture Search (NAS). To
cope with this problem, we propose an Architecture-Driven Weight Prediction
(ADWP) approach for neural architecture search (NAS). In our approach, we first
design an architecture-intensive search space and then train a HyperNetwork by
inputting stochastic encoding architecture parameters. In the trained
HyperNetwork, weights of convolution kernels can be well predicted for neural
architectures in the search space. Consequently, the target architectures can
be evaluated efficiently without any finetuning, thus enabling us to search
fortheoptimalarchitectureinthespaceofgeneralnetworks (macro-search). Through
real experiments, we evaluate the performance of the models discovered by the
proposed AD-WPNAS and results show that one search procedure can be completed
in 4.0 GPU hours on CIFAR-10. Moreover, the discovered model obtains a test
error of 2.41% with only 1.52M parameters which is superior to the best
existing models.
