Fully Self-Supervised Out-of-Domain Few-Shot Learning with Masked Autoencoders - Reece Walsh; Islam Osman; Omar Abdelaziz; Mohamed S. Shehata (Journal of Imaging, 2024)
Few-shot learning aims to identify unseen classes with limited labelled data. Recent few-shot learning techniques have shown success in generalizing to unseen classes; however, the performance of these techniques has also been shown to degrade when tested on an out-of-domain setting. Previous work, additionally, has also demonstrated increasing reliance on supervised finetuning in an off-line or online capacity. This paper proposes a novel, fully self-supervised few-shot learning technique (FSS) that utilizes a vision transformer and masked autoencoder. The proposed technique can generalize to out-of-domain classes by finetuning the model in a fully self-supervised method for each episode. We evaluate the proposed technique using three datasets (all out-of-domain). As such, our results show that FSS has an accuracy gain of <semantics>1.05%</semantics>, <semantics>0.12%</semantics>, and <semantics>1.28%</semantics> on the ISIC, EuroSat, and BCCD datasets, respectively, without the use of supervised training.