Neural Inference Search for Multiloss Segmentation Models

Sam Slade, Li Zhang*, Haoqian Huang, Houshyar Asadi, Chee Peng Lim, Yonghong Yu, Dezong Zhao, Hanhe Lin, Rong Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
15 Downloads (Pure)


-dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date16 Jun 2023
Publication statusE-pub ahead of print - 16 Jun 2023

Cite this