Theory-Inspired Path-Regularized Differential Network Architecture Search

TL;DR

This paper provides a theoretical analysis of differential architecture search (DARTS), revealing why skip connections dominate and proposing a path-regularized DARTS method to improve search diversity and performance.

Contribution

The work offers the first theoretical analysis of operation effects in DARTS and introduces a novel path-regularized DARTS with modules to enhance search fairness and exploration.

Findings

Skip connections lead to faster convergence in DARTS.

Path-regularized DARTS improves architecture search outcomes.

Experimental validation on image classification tasks shows superior performance.

Abstract

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly reveals the impact of skip connections to fast network optimization and its competitive advantage over other types of operations in DARTS. Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during the search. Experimental results on image classification tasks validate its advantages.