Foundation Models

Masked Autoencoder (MAE)

A masked autoencoder (MAE) is a self-supervised vision pretraining method, introduced by He et al. in 2022, that masks a large fraction of image patches, typically 75 percent, and trains a vision transformer to reconstruct the missing pixels. An asymmetric design in which the encoder sees only visible patches makes pretraining efficient. The learned representations transfer strongly to downstream recognition and control tasks.

Why it matters for physical AI

Masked-reconstruction pretraining on human video, as in the MVP line of work, produces visual encoders that markedly improve sample efficiency when reused as frozen or fine-tuned backbones for manipulation policies.