AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision
of Weight Sharing
- URL: http://arxiv.org/abs/2108.03001v1
- Date: Fri, 6 Aug 2021 08:31:42 GMT
- Title: AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision
of Weight Sharing
- Authors: Yuge Zhang and Chenqian Yan and Quanlu Zhang and Li Lyna Zhang and
Yaming Yang and Xiaotian Gao and Yuqing Yang
- Abstract summary: We introduce Learning to Rank methods to select the best (ace) architectures from a space.
We also propose to leverage weak supervision from weight sharing by pretraining architecture representation on weak labels obtained from the super-net.
Experiments on NAS benchmarks and large-scale search spaces demonstrate that our approach outperforms SOTA with a significantly reduced search cost.
- Score: 6.171090327531059
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Architecture performance predictors have been widely used in neural
architecture search (NAS). Although they are shown to be simple and effective,
the optimization objectives in previous arts (e.g., precise accuracy estimation
or perfect ranking of all architectures in the space) did not capture the
ranking nature of NAS. In addition, a large number of ground-truth
architecture-accuracy pairs are usually required to build a reliable predictor,
making the process too computationally expensive. To overcome these, in this
paper, we look at NAS from a novel point of view and introduce Learning to Rank
(LTR) methods to select the best (ace) architectures from a space.
Specifically, we propose to use Normalized Discounted Cumulative Gain (NDCG) as
the target metric and LambdaRank as the training algorithm. We also propose to
leverage weak supervision from weight sharing by pretraining architecture
representation on weak labels obtained from the super-net and then finetuning
the ranking model using a small number of architectures trained from scratch.
Extensive experiments on NAS benchmarks and large-scale search spaces
demonstrate that our approach outperforms SOTA with a significantly reduced
search cost.
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