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Network Architecture Search (NAS) has achieved impressive results and generated models comparable with humans' classifications. Automating the definition of a neural architecture reduces the need for expert work efforts and mitigates human bias from architecture design. NAS techniques usually consist of an algorithm to search for the best architecture in a predetermined space of parameters or functions. Due to the number of deep neural architectures' parameters, this search space includes millions of parameters, which makes NAS a cost procedure and may lead the search to overfit the training set. To reduce NAS search spaces' complexity and still obtain competitive results, we propose CoNAS, a committee of NAS-based models, by restricting the search spaces to perform Differentiable ARchiTecture Search (DARTS). Our results point to improved accuracy over DARTS on CIFAR-10, training the networks from scratch. and Imagnette, using a transfer learning approach. |
