AutoML libraries such as AutoKeras and KerasTuner can perform neural architecture search (NAS) by training a supernet and sampling candidate convolutional networks from it. When weight sharing is enabled so that each candidate reuses parameters already learned in the supernet instead of being trained from scratch, what primary advantage does this strategy give the AutoML pipeline?
It removes the need for a separate validation dataset because the in-sample training loss is sufficient.
It guarantees that every possible architecture in the search space is explored exhaustively.
It drastically cuts search time and GPU cost while maintaining comparable accuracy to training each candidate separately.
It merges the parameters of all sampled paths into the final model, giving it higher capacity than any single candidate network.
Weight sharing trains one over-parameterized supernet and reuses its weights when evaluating each sampled sub-network. Because the expensive gradient updates happen only once, the AutoML system can explore many architectures while spending only a fraction of the GPU time and energy that would be required to train every candidate model independently. The technique speeds the search and lowers compute cost but does not make the search exhaustive, eliminate the need for a validation set, or enlarge the capacity of the final exported model, which keeps only the weights for the selected path.
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