Efficient Neural Architecture Search Using Graph-Based Metrics Without Training

Core Concepts

NASGraph, a training-free and data-agnostic neural architecture search method, maps neural architectures to graphs and uses graph measures as proxy metrics to efficiently rank and search for optimal architectures.

Abstract

The paper proposes NASGraph, a novel framework for neural architecture search (NAS) that maps neural architectures to graphs and uses graph measures as proxy metrics to rank the architectures without the need for training or task-specific data.

Key highlights:

• NASGraph converts neural architectures into directed acyclic graphs (DAGs) by treating neural components as graph nodes and their relationships as edges.
• It then computes graph measures, such as average degree, as proxy metrics to rank the architectures without training.
• Extensive experiments on NAS benchmarks like NAS-Bench-101, NAS-Bench-201, TransNAS-Bench-101, and NDS show that NASGraph achieves competitive performance compared to existing training-free NAS methods.
• NASGraph is data-agnostic and computationally lightweight, running on CPUs instead of GPUs.
• Combining NASGraph's average degree metric with the data-dependent jacob_cov metric further improves the ranking correlation with the true architecture performance.
• Analysis reveals that NASGraph has the lowest bias towards specific neural operations compared to other training-free NAS methods.

Overall, the paper presents a novel graph-based perspective on NAS that enables efficient and unbiased architecture search without the need for training or task-specific data.

Stats

NASGraph can find the best architecture among 200 randomly sampled architectures from NAS-Bench-201 in 217 CPU seconds.
On NAS-Bench-201, NASGraph's average degree metric achieves Spearman's rank correlation ρ of 0.78, 0.80, and 0.77 on CIFAR-10, CIFAR-100, and ImageNet-16-120 datasets, respectively.
On the NDS benchmark, NASGraph's average degree metric achieves Kendall's Tau rank correlation τ of 0.32, 0.45, 0.41, 0.37, and 0.40 on the AMOEBA, DARTS, ENAS, NASNet, and PNAS search spaces, respectively.

Quotes

"NASGraph maps the neural architecture space to the graph space. To our best knowledge, this is the first work to apply graph theory for NAS."
"Using the extracted graph measures for NAS, NASGraph achieves competitive performance on NAS-Bench-101, NAS-Bench-201, Micro TransNAS-Bench-101 and NDS benchmarks, when compared to existing training-free NAS methods."
"In comparison to existing training-free NAS techniques, we show that the computation of NASGraph is lightweight (only requires CPU)."

Key Insights Distilled From

Graph is all you need? Lightweight data-agnostic neural architecture search without training

by Zhenhan Huan... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01306.pdf

Deeper Inquiries

How can the proposed NASGraph framework be extended to handle more complex neural architecture search spaces beyond the benchmarks considered in this study

The NASGraph framework can be extended to handle more complex neural architecture search spaces by incorporating additional graph-theoretic measures and refining the graph conversion process. One way to extend the framework is to consider more intricate relationships between graph nodes, such as higher-order connectivity patterns or graph motifs. By capturing these complex relationships, the framework can better represent the structural characteristics of diverse neural architectures. Additionally, incorporating graph clustering algorithms can help identify substructures within the neural architecture space, enabling more targeted exploration and optimization. Furthermore, integrating reinforcement learning techniques into the NASGraph framework can enhance the search process by guiding the exploration towards promising regions of the search space. By combining these advanced techniques, NASGraph can effectively handle more complex neural architecture search spaces with improved efficiency and effectiveness.

What other graph-theoretic measures, beyond average degree, could be explored as proxy metrics for efficient neural architecture search

In addition to average degree, several other graph-theoretic measures can be explored as proxy metrics for efficient neural architecture search. One potential measure is graph density, which quantifies the proportion of existing edges to the total possible edges in a graph. High-density graphs may indicate more interconnected neural architectures with potentially better performance. Another measure is graph centrality, which identifies the most influential nodes in a graph. By considering the centrality of nodes in the neural architecture graph, the framework can prioritize architectures with key components that contribute significantly to overall performance. Moreover, graph entropy can be utilized to assess the complexity and diversity of connections within neural architectures, providing insights into the robustness and adaptability of different designs. By incorporating these diverse graph measures, NASGraph can leverage a comprehensive set of metrics to evaluate and rank neural architectures effectively.

Can the insights from the bias analysis towards neural operations be used to guide the design of more effective neural architecture search algorithms

The insights from the bias analysis towards neural operations can indeed guide the design of more effective neural architecture search algorithms. By understanding the preferences and biases of different NAS methods towards specific operations, researchers can develop strategies to mitigate biases and promote diversity in the search process. For example, techniques such as operation diversification can be employed to ensure a balanced exploration of various architectural components, preventing the dominance of certain operations in the search results. Additionally, incorporating regularization mechanisms that penalize the over-representation of specific operations can help promote the discovery of novel and effective architectures. By leveraging the insights from bias analysis, NAS algorithms can be fine-tuned to encourage exploration of diverse architectural configurations, leading to the discovery of more robust and high-performing neural network models.