**NEAT**, or **Neural Attention Fields**, is a feature representation for end-to-end imitation learning models. NEAT is a continuous function which maps locations in Bird's Eye View (BEV) scene coordinates to waypoints and semantics, using intermediate attention maps to iteratively compress high-dimensional 2D image features into a compact representation. This allows the model to selectively attend to relevant regions in the input while ignoring information irrelevant to the driving task, effectively associating the images with the BEV representation. Furthermore, visualizing the attention maps for models with NEAT intermediate representations provides improved interpretability.

This method applies Polya-Gamma latent variables as a way to obtain closed form expressions for full-conditionals of posterior distributions in sampling algorithms like MCMC.

Polya-Gamma Augmentation

Bayesian inference for logistic models using Polya-Gamma latent variables

NEAT

NEAT: Neural Attention Fields for End-to-End Autonomous Driving

A variant of [CutMix](https://paperswithcode.com/method/cutmix) which randomly samples masks from Fourier space.

Source	NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com