all AI news
AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework
Feb. 20, 2024, 5:44 a.m. | Zikai Zhou, Shuo Zhang, Ziruo Wang, Huanran Chen
cs.LG updates on arXiv.org arxiv.org
Abstract: The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvantages. In response, efforts have been made to design a unified normalization function that combines all normalization procedures and mitigates their weaknesses. We also proposed a new normalization function called Adaptive Fusion Normalization. Through experiments, we demonstrate AFN outperforms the previous normalization techniques in domain generalization and image classification tasks.
abstract advantages arxiv cs.cv cs.lg decoder deep learning design disadvantages encoder encoder-decoder framework function functions fusion normalization researchers success them type via
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US