  Differentiable architecture search (DARTS) is a prevailing NAS solution to
identify architectures. Based on the continuous relaxation of the architecture
space, DARTS learns a differentiable architecture weight and largely reduces
the search cost. However, its stability has been challenged for yielding
deteriorating architectures as the search proceeds. We find that the
precipitous validation loss landscape, which leads to a dramatic performance
drop when distilling the final architecture, is an essential factor that causes
instability. Based on this observation, we propose a perturbation-based
regularization - SmoothDARTS (SDARTS), to smooth the loss landscape and improve
the generalizability of DARTS-based methods. In particular, our new
formulations stabilize DARTS-based methods by either random smoothing or
adversarial attack. The search trajectory on NAS-Bench-1Shot1 demonstrates the
effectiveness of our approach and due to the improved stability, we achieve
performance gain across various search spaces on 4 datasets. Furthermore, we
mathematically show that SDARTS implicitly regularizes the Hessian norm of the
validation loss, which accounts for a smoother loss landscape and improved
performance.
