  For an unknown (new) classification dataset, choosing an appropriate deep
learning architecture is often a recursive, time-taking, and laborious process.
In this research, we propose a novel technique to recommend a suitable
architecture from a repository of known models. Further, we predict the
performance accuracy of the recommended architecture on the given unknown
dataset, without the need for training the model. We propose a model encoder
approach to learn a fixed length representation of deep learning architectures
along with its hyperparameters, in an unsupervised fashion. We manually curate
a repository of image datasets with corresponding known deep learning models
and show that the predicted accuracy is a good estimator of the actual
accuracy. We discuss the implications of the proposed approach for three
benchmark images datasets and also the challenges in using the approach for
text modality. To further increase the reproducibility of the proposed
approach, the entire implementation is made publicly available along with the
trained models.
