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

**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.

NEAT

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

FMix

FMix: Enhancing Mixed Sample Data Augmentation

Spatio-temporal features extraction that measure the stabilty. The proposed method is based on a compression algorithm named Run Length Encoding. The workflow of the method is presented bellow.

Source	FMix: Enhancing Mixed Sample Data Augmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com