论文标题
$α$飞镖再次：通过掩盖图像建模增强可区分架构搜索
$α$ DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
论文作者
论文摘要
可区分的架构搜索（飞镖）一直是自动机器学习的主流方向。由于发现原始飞镖将不可避免地会融合到贫穷的建筑中，因此最近的作品通过设计基于规则的建筑选择技术或合并复杂的正则化技术来减轻这一点，从而放弃了基于最大的参数价值的原始飞镖的简单性，即$α$。此外，我们发现所有以前的尝试都仅依赖分类标签，因此仅学习单个模态信息并限制了共享网络的表示能力。为此，我们建议通过制定补丁恢复方法来另外注入语义信息。具体而言，我们利用了最近的趋势屏蔽图像建模，并且在搜索阶段不放弃下游任务的指导。我们的方法超过了所有以前的飞镖变体，并且在没有复杂的手动设计策略的情况下，在CIFAR-10，CIFAR-100和Imagenet上实现了最先进的结果。
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 $α$. 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.
