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Training a good supernet in one-shot NAS methods is difficult since thesearch space is usually considerably huge (e.g., $13^{21}$). In order toenhance the supernet's evaluation ability, one greedy strategy is to samplegood paths, and let the supernet lean towards the good ones and ease itsevaluation burden as a result. However, in practice the search can be stillquite inefficient since the identification of good paths is not accurate enoughand sampled paths still scatter around the whole search space. In this paper,we leverage an explicit path filter to capture the characteristics of paths anddirectly filter those weak ones, so that the search can be thus implemented onthe shrunk space more greedily and efficiently. Concretely, based on the factthat good paths are much less than the weak ones in the space, we argue thatthe label of "weak paths" will be more confident and reliable than that of"good paths" in multi-path sampling. In this way, we thus cast the training ofpath filter in the positive and unlabeled (PU) learning paradigm, and alsoencourage a \textit{path embedding} as better path/operation representation toenhance the identification capacity of the learned filter. By dint of thisembedding, we can further shrink the search space by aggregating similaroperations with similar embeddings, and the search can be more efficient andaccurate. Extensive experiments validate the effectiveness of the proposedmethod GreedyNASv2. For example, our obtained GreedyNASv2-L achieves $81.1\%$Top-1 accuracy on ImageNet dataset, significantly outperforming the ResNet-50strong baselines.

GreedyNASv2: Greedier Search with a Greedy Path Filter