Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
Researchers have developed LH-NeF, a new framework for learning tokenized representations of continuous signals using neural fields. This approach incorporates hierarchy and spatial locality priors, enabling a feed-forward encoding method that significantly reduces memory usage and increases batch sizes compared to previous meta-learning techniques. LH-NeF demonstrates strong performance across various data types, including images, 3D shapes, and climate fields, matching or surpassing existing specialized and general baselines. AI
IMPACT Introduces a more memory-efficient and scalable method for learning representations from continuous signals using neural fields.