A General-Purpose Transferable Predictor for Neural Architecture Search

• URL: http://arxiv.org/abs/2302.10835v1
• Date: Tue, 21 Feb 2023 17:28:05 GMT
• Title: A General-Purpose Transferable Predictor for Neural Architecture Search
• Authors: Fred X. Han, Keith G. Mills, Fabian Chudak, Parsa Riahi, Mohammad Salameh, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu
• Abstract summary: We propose a general-purpose neural predictor for Neural Architecture Search (NAS) that can transfer across search spaces. Experimental results on NAS-Bench-101, 201 and 301 demonstrate the efficacy of our scheme.
• Score: 22.883809911265445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and achieve high ranking correlations between predicted and ground truth performance in several NAS benchmarks. However, existing predictors are often designed based on network encodings specific to a predefined search space and are therefore not generalizable to other search spaces or new architecture families. In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators. We further combine our CG network representation with Contrastive Learning (CL) and propose a graph representation learning procedure that leverages the structural information of unlabeled architectures from multiple families to train CG embeddings for our performance predictor. Experimental results on NAS-Bench-101, 201 and 301 demonstrate the efficacy of our scheme as we achieve strong positive Spearman Rank Correlation Coefficient (SRCC) on every search space, outperforming several Zero-Cost Proxies, including Synflow and Jacov, which are also generalizable predictors across search spaces. Moreover, when using our proposed general-purpose predictor in an evolutionary neural architecture search algorithm, we can find high-performance architectures on NAS-Bench-101 and find a MobileNetV3 architecture that attains 79.2% top-1 accuracy on ImageNet.

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