Self-Supervised Learning for Generalizable AI: Bridging the Gap Between Pretraining and Real-World Deployment

Abstract

Representation learning received a breakthrough with self-supervised learning (SSL) because it uses big unlabeled datasets to remove the need for human annotation. The robustness of SSL models weakens quickly during real-world deployment because they struggle to perform well in domain shift situations and out-of-distribution (OOD) environments. The critical limitation of poor generalization across different environments receives a solution from this research through the creation of Causal-Contrastive Self-Supervised Learning (C2SSL) which merges contrastive learning with causal inference to discover invariant features. The main breakthrough stems from constructing causal models of data structures to make models focus on constant features while decreasing the impact of spurious pattern correlations that normally degrade generalization results. The evaluation of our method occurs in three significant domains including healthcare diagnostics with MIMIC-III data and autonomous driving with nuScenes data and industrial anomaly detection with MVTec AD. The experimental outcomes demonstrate that C2SSL achieves superior performance compared to existing SSL modules through better results in prediction accuracy and OOD detection capabilities as well as ease of interpretation. Ablation tests and t-SNE layouts combined with performance analysis under different data conditions show the operational effectiveness of our proposed approach in experiments. Self-supervised models gain momentum in operational deployment through this achievement because it offers multiple benefits including quick implementation in real-world environments.