Distilldarts: Network Distillation for Smoothing Gradient Distributions in Differentiable Architecture Search

Recent studies show that differentiable architecture search (DARTS) suffers notable instability and collapse issue: skip-connect may gradually dominate the cell, leading to deteri-orating architectures. We conjecture that the domination of skip-connect is due to its superiority in gradient compen-sate. On this foundation, we propose a novel and stable method, called DistillDARTS, to stabilize DARTS by knowl-edge distillation and self-distillation scheme. Specifically, the distillation is able to serve as a substitute for skip-connect and smooth the back-propagated gradient distributions among layers of DARTS. By compensating gradients in shallow lay-ers, our method can relieve the dependence of gradient on skip-connect and hence mitigates the collapse issue. Exten-sive experiments on a range of benchmarks demonstrate that DistillDARTS can obtain sturdy architectures with few skip-connects without additional manual interventions, thus suc-cessfully improving the robustness of DARTS. Due to the im-proved stability, our proposed approach achieves the accuracy of 97.57% on CIFAR-10 and 75.8% on ImageNet.