  Automated neural network design has received ever-increasing attention with
the evolution of deep convolutional neural networks (CNNs), especially
involving their deployment on embedded and mobile platforms. One of the biggest
problems that neural architecture search (NAS) confronts is that a large number
of candidate neural architectures are required to train, using, for instance,
reinforcement learning and evolutionary optimisation algorithms, at a vast
computation cost. Even recent differentiable neural architecture search (DNAS)
samples a small number of candidate neural architectures based on the
probability distribution of learned architecture parameters to select the final
neural architecture. To address this computational complexity issue, we
introduce a novel \emph{architecture parameterisation} based on scaled sigmoid
function, and propose a general \emph{Differentiable Neural Architecture
Learning} (DNAL) method to optimize the neural architecture without the need to
evaluate candidate neural networks. Specifically, for stochastic supernets as
well as conventional CNNs, we build a new channel-wise module layer with the
architecture components controlled by a scaled sigmoid function. We train these
neural network models from scratch. The network optimization is decoupled into
the weight optimization and the architecture optimization. We address the
non-convex optimization problem of neural architecture by the continuous scaled
sigmoid method with convergence guarantees. Extensive experiments demonstrate
our DNAL method delivers superior performance in terms of neural architecture
search cost. The optimal networks learned by DNAL surpass those produced by the
state-of-the-art methods on the benchmark CIFAR-10 and ImageNet-1K dataset in
accuracy, model size and computational complexity.
