FX-DARTS: Designing Topology-Unconstrained Architectures With Differentiable Architecture Search and...

Strong priors are imposed on the search space of differentiable architecture search (DARTS), such that cells of the same type share the same topological structure and each intermediate node retains two operators from distinct nodes. While these priors reduce optimization difficulties and improve the applicability of searched architectures, they hinder the subsequent development of automated machine learning (auto-ML) and prevent the optimization algorithm from exploring more powerful neural networks through improved architectural flexibility. This article aims to reduce these prior constraints by eliminating restrictions on cell topology and modifying the discretization mechanism for super-networks. Specifically, the flexible DARTS (FX-DARTS) method, which leverages an entropy-based super-network shrinking (ESS) framework, is presented to address the challenges arising from the elimination of prior constraints. Notably, FX-DARTS enables the derivation of neural architectures without strict prior rules while maintaining the stability in the enlarged search space. Experimental results on image classification benchmarks demonstrate that FX-DARTS is capable of exploring a set of neural architectures with competitive trade-offs between performance and computational complexity within a single search procedure.