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For an unknown (new) classification dataset, choosing an appropriate deeplearning architecture is often a recursive, time-taking, and laborious process.In this research, we propose a novel technique to recommend a suitablearchitecture from a repository of known models. Further, we predict theperformance accuracy of the recommended architecture on the given unknowndataset, without the need for training the model. We propose a model encoderapproach to learn a fixed length representation of deep learning architecturesalong with its hyperparameters, in an unsupervised fashion. We manually curatea repository of image datasets with corresponding known deep learning modelsand show that the predicted accuracy is a good estimator of the actualaccuracy. We discuss the implications of the proposed approach for threebenchmark images datasets and also the challenges in using the approach fortext modality. To further increase the reproducibility of the proposedapproach, the entire implementation is made publicly available along with thetrained models.

"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets