Disentangled Neural Architecture Search

TL;DR

Disentangled Neural Architecture Search (DNAS) introduces an interpretable framework that disentangles architecture representations, enabling targeted, efficient search for high-performance neural networks under various constraints.

Contribution

DNAS is the first method to disentangle architecture representations, improving interpretability and search efficiency in neural architecture search.

Findings

Achieves state-of-the-art 94.21% on NASBench-101 with less than 1/13 computational cost

Disentangles architecture components like operations, skip connections, and layers

Flexible architecture search under different FLOPS restrictions

Abstract

Neural architecture search has shown its great potential in various areas recently. However, existing methods rely heavily on a black-box controller to search architectures, which suffers from the serious problem of lacking interpretability. In this paper, we propose disentangled neural architecture search (DNAS) which disentangles the hidden representation of the controller into semantically meaningful concepts, making the neural architecture search process interpretable. Based on systematical study, we discover the correlation between network architecture and its performance, and propose a dense-sampling strategy to conduct a targeted search in promising regions that may generate well-performing architectures. We show that: 1) DNAS successfully disentangles the architecture representations, including operation selection, skip connections, and number of layers. 2) Benefiting from interpretability, DNAS can find excellent architectures under different FLOPS restrictions flexibly. 3) Dense-sampling leads to neural architecture search with higher efficiency and better performance. On the NASBench-101 dataset, DNAS achieves state-of-the-art performance of 94.21% using less than 1/13 computational cost of baseline methods. On ImageNet dataset, DNAS discovers the competitive architectures that achieves 22.7% test error. our method provides a new perspective of understanding neural architecture search.