HEP-NAS partitions a NAS supernet by connection hierarchies, trains the candidate branches with mutual distillation, and greedily keeps the best branch, reporting lower error than prior few-shot NAS methods on standard benchmarks.
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HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning
HEP-NAS partitions a NAS supernet by connection hierarchies, trains the candidate branches with mutual distillation, and greedily keeps the best branch, reporting lower error than prior few-shot NAS methods on standard benchmarks.