XViT-Net: An Explainable Self-Supervised Vision Transformer Framework for Image Recognition

Abstract

XViT-Net tackles the issues of data reliance and interpretability in modern image recognition systems with its groundbreaking explainable self-supervised Vision Transformer framework. Instead of depending on large-scale labelled datasets and providing limited transparency, XViT-Net incorporates explainable AI mechanisms directly into the transformer architecture and uses self-supervised pretraining to learn rich and transferrable visual representations from unlabeled data. For strong feature learning with minimal annotation cost, contrastive and reconstruction-driven learning goals integrate global semantic context with fine-grained visual details. In order to boost confidence and model transparency, XViT-Net employs attention-based explainability modules to draw attention to discriminative picture areas that influence recognition decisions. It then provides visual explanations that humans can understand without sacrificing performance. After running thorough benchmark image recognition trials, XViT-Net proved to be more accurate, resilient, and data efficient than the fully supervised Vision Transformer baselines. The validity and consistency of explanations are confirmed through both quantitative and qualitative analyses, establishing XViT-Net as a trustworthy, interpretable, and data-efficient foundation for future image recognition systems.