  Widespread applications of deep learning have led to a plethora of
pre-trained neural network models for common tasks. Such models are often
adapted from other models via transfer learning. The models may have varying
training sets, training algorithms, network architectures, and
hyper-parameters. For a given application, what isthe most suitable model in a
model repository? This is a critical question for practical deployments but it
has not received much attention. This paper introduces the novel problem of
searching and ranking models based on suitability relative to a target dataset
and proposes a ranking algorithm called \textit{neuralRank}. The key idea
behind this algorithm is to base model suitability on the discriminating power
of a model, using a novel metric to measure it. With experimental results on
the MNIST, Fashion, and CIFAR10 datasets, we demonstrate that (1) neuralRank is
independent of the domain, the training set, or the network architecture and
(2) that the models ranked highly by neuralRank ranking tend to have higher
model accuracy in practice.
