A myriad of recent breakthroughs in hand-crafted neural architectures for visual recognition have highlighted the urgent need to explore hybrid architectures consisting of diversified building blocks. Meanwhile, neural architecture search methods are surging with an expectation to reduce human efforts. However, whether NAS methods can efficiently and effectively handle diversified search spaces with disparate candidates (e.g. CNNs and transformers) is still an open question. In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of inaccurate architecture rating caused by large weight-sharing space and biased supervision in previous methods. More specifically, we factorize the search space into blocks and utilize a novel self-supervised training scheme, named ensemble bootstrapping, to train each block separately before searching them as a whole towards the population center. Additionally, we present HyTra search space, a fabric-like hybrid CNN-transformer search space with searchable down-sampling positions. On this challenging search space, our searched model, BossNet-T, achieves up to 82.2% accuracy on ImageNet, surpassing EfficientNet by 2.1% with comparable compute time. Moreover, our method achieves superior architecture rating accuracy with 0.78 and 0.76 Spearman correlation on the canonical MBConv search space with ImageNet and on NATS-Bench size search space with CIFAR-100, respectively, surpassing state-of-the-art NAS methods. Code and pretrained models are available at https://github.com/changlin31/BossNAS .

本文提出了Block-wisely Self-supervised Neural Architecture Search (BossNAS)方法，包括定义可搜索的CNN-transformer混合结构搜索空间HyTra、以及新型自监督训练策略ensemble bootstrapping。在此挑战性的搜索空间上，作者搜索得到的模型BossNet-T在ImageNet上可达到82.5%的准确率，且在MBConv和NATS-Bench大小的搜索空间上均有较高的最优架构效果。

使用块智能自监督神经架构搜索探索混合CNN转换器