Automated deep learning architecture design using differentiable architecture search (DARTS)
Creating neural networks by hand is a slow trial-and-error based process. Designing new architectures similar to GoogleNet or FractalNets, which use repeated tree-based structures, is highly likely to be inefficient and sub-optimal because of the large number of possibilities for composing such structures. Recently, neural architecture search algorithms have been able to automate the process of architecture design and have often attained state-of-the-art performances on CIFAR-10, ImageNet and Penn Tree Bank datasets. Even though the search time has been reduced to tens of GPU hours from tens of ...
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