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Differentiable architecture search (DARTS) has been a mainstream direction inautomatic machine learning. Since the discovery that original DARTS willinevitably converge to poor architectures, recent works alleviate this byeither designing rule-based architecture selection techniques or incorporatingcomplex regularization techniques, abandoning the simplicity of the originalDARTS that selects architectures based on the largest parametric value, namely$\alpha$. Moreover, we find that all the previous attempts only rely onclassification labels, hence learning only single modal information andlimiting the representation power of the shared network. To this end, wepropose to additionally inject semantic information by formulating a patchrecovery approach. Specifically, we exploit the recent trending masked imagemodeling and do not abandon the guidance from the downstream tasks during thesearch phase. Our method surpasses all previous DARTS variants and achievesstate-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet without complexmanual-designed strategies.

$\alpha$ DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling