FEAR: A Simple Lightweight Method to Rank Architectures

Abstract

The fundamental problem in Neural Architecture Search (NAS) is to efficiently find high-performing architectures from a given search space. We propose a simple but powerful method which we call FEAR, for ranking architectures in any search space. FEAR leverages the viewpoint that neural networks are powerful non-linear feature extractors. First, we train different architectures in the search space to the same training or validation error. Then, we compare the usefulness of the features extracted by each architecture. We do so with a quick training keeping most of the architecture frozen. This gives fast estimates of the relative performance. We validate FEAR on Natsbench topology search space on three different datasets against competing baselines and show strong ranking correlation especially compared to recently proposed zero-cost methods. FEAR particularly excels at ranking high-performance architectures in the search space. When used in the inner loop of discrete search algorithms like random search, FEAR can cut down the search time by approximately 2.4X without losing accuracy. We additionally empirically study very recently proposed zero-cost measures for ranking and find that they breakdown in ranking performance as training proceeds and also that data-agnostic ranking scores which ignore the dataset do not generalize across dissimilar datasets.

31 pages, 8 figures

References in corpus (7)

• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
• Similarity of Neural Network Representations Revisited
• A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
• NAS-Bench-101: Towards Reproducible Neural Architecture Search
• Rethinking Architecture Selection in Differentiable NAS
• Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition
• Efficient Model Performance Estimation via Feature Histories