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 framework that jointly optimizes masked image reconstruction and classification using both unlabeled and labeled samples with dynamically selected pseudo-labels. SSMAE introduces a validation-driven gating mechanism that activates pseudo-labeling only after the model achieves reliable, high-confidence predictions that are consistent across both weakly and strongly augmented views of the same image, reducing confirmation bias. On CIFAR-10 and CIFAR-100, SSMAE consistently outperforms supervised ViT and fine-tuned MAE, with the largest gains in low-label regimes (+9.24% over ViT on CIFAR-10 with 10% labels). Our results demonstrate that when pseudo-labels are introduced is as important as how they are generated for data-efficient transformer training. Codes are available at https://github.com/atik666/ssmae.
@article{arxiv.2601.20072,
title = {Semi-Supervised Masked Autoencoders: Unlocking Vision Transformer Potential with Limited Data},
author = {Atik Faysal and Mohammad Rostami and Reihaneh Gh. Roshan and Nikhil Muralidhar and Huaxia Wang},
journal= {arXiv preprint arXiv:2601.20072},
year = {2026}
}