  In this paper, we point out that differential architecture search (DARTS)
makes gradient of architecture parameters biased for network weights and
architecture parameters are updated in different datasets alternatively in the
bi-level optimization framework. The bias causes the architecture parameters of
non-learnable operations to surpass that of learnable operations. Moreover,
using softmax as architecture parameters' activation function and inappropriate
learning rate would exacerbate the bias. As a result, it's frequently observed
that non-learnable operations are dominated in the search phase. To reduce the
bias, we propose to use single-level to replace bi-level optimization and
non-competitive activation function like sigmoid to replace softmax. As a
result, we could search high-performance architectures steadily. Experiments on
NAS Benchmark 201 validate our hypothesis and stably find out nearly the
optimal architecture. On DARTS space, we search the state-of-the-art
architecture with 77.0% top1 accuracy (training setting follows PDARTS and
without any additional module) on ImageNet-1K and steadily search architectures
up-to 76.5% top1 accuracy (but not select the best from the searched
architectures) which is comparable with current reported best result.
