Improving Neural Architecture Search Image Classifiers via Ensemble Learning - implementation -

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Improving Neural Architecture Search Image Classifiers via Ensemble Learning by Vladimir Macko, Charles Weill, Hanna Mazzawi and Javier Gonzalvo‎

AdaNAS is an algorithm for learning an ensemble that improves the performance of neural architecture search models while having a similar parameter count as single large model. Our experiments demonstrate that these ensembles improve accuracy upon a single neural network of the same size. Our models achieve comparable results with the state-of-the-art on CIFAR-10 and set a new state-of-the-art on CIFAR-100.

An implementation is ehre: https://github.com/tensorflow/adanet/tree/master/research/improve_nas

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