  Differentiable architecture search (DARTS) has been a mainstream direction in
automatic machine learning. Since the discovery that original DARTS will
inevitably converge to poor architectures, recent works alleviate this by
either designing rule-based architecture selection techniques or incorporating
complex regularization techniques, abandoning the simplicity of the original
DARTS that selects architectures based on the largest parametric value, namely
$\alpha$. Moreover, we find that all the previous attempts only rely on
classification labels, hence learning only single modal information and
limiting the representation power of the shared network. To this end, we
propose to additionally inject semantic information by formulating a patch
recovery approach. Specifically, we exploit the recent trending masked image
modeling and do not abandon the guidance from the downstream tasks during the
search phase. Our method surpasses all previous DARTS variants and achieves
state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet without complex
manual-designed strategies.
