Semi-Supervised Masked Autoencoders: Unlocking Vision Transformer Potential with Limited Data

Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan +2

We address the challenge of training Vision Transformers (ViTs) when labeled data is scarce but unlabeled data is abundant. We propose Semi-Supervised Masked Autoencoder (SSMAE), a…