1 code implementation • ICLR 2021 • Hayeon Lee, Eunyoung Hyung, Sung Ju Hwang
Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not generalize well across multiple tasks (datasets).
1 code implementation • NeurIPS 2021 • Wonyong Jeong, Hayeon Lee, Gun Park, Eunyoung Hyung, Jinheon Baek, Sung Ju Hwang
To address such limitations, we introduce a novel problem of \emph{Neural Network Search} (NNS), whose goal is to search for the optimal pretrained network for a novel dataset and constraints (e. g. number of parameters), from a model zoo.