ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks
Kartikeya Bhardwaj Hsin-Pai Cheng Sweta Priyadarshi Zhuojin Li
Zero-Shot Neural Architecture Search (NAS) approaches propose novel
training-free metrics called zero-shot proxies to substantially reduce the
search time compared to the traditional training-based NAS. Despite the success
on image classification, the effectiveness of zero-shot proxies is rarely
evaluated on complex vision tasks such as semantic segmentation and object
detection. Moreover, existing zero-shot proxies are shown to be biased towards
certain model characteristics which restricts their broad applicability. In
this paper, we empirically study the bias of state-of-the-art (SOTA) zero-shot
proxy ZiCo across multiple vision tasks and observe that ZiCo is biased towards
thinner and deeper networks, leading to sub-optimal architectures. To solve the
problem, we propose a novel bias correction on ZiCo, called ZiCo-BC. Our
extensive experiments across various vision tasks (image classification, object
detection and semantic segmentation) show that our approach can successfully
search for architectures with higher accuracy and significantly lower latency
on Samsung Galaxy S10 devices.