Abstract
This paper presents the DNA Family, a new framework for boosting the effectiveness of weight-sharing Neural Architecture Search (NAS) by dividing large search spaces into smaller blocks and applying block-wise supervisions. The approach demonstrates high performance on benchmarks such as ImageNet, surpassing previous NAS techniques in accuracy and efficiency.
Type
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Xiaojun Chang
常晓军教授/主任
My research interests include Artificial Intelligence, Machine Learning and Multimedia.