Disentangled Neural Architecture Search
This addresses the problem of opaque and inefficient architecture search for AI researchers, offering an interpretable and efficient method, though it is incremental in improving existing NAS approaches.
The paper tackles the lack of interpretability in neural architecture search by proposing DNAS, which disentangles controller representations into meaningful concepts and uses dense-sampling for targeted search, achieving state-of-the-art performance of 94.21% on NASBench-101 with less than 1/13 computational cost and competitive results on ImageNet.
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.