Semi Supervised Adversarial

Semi-supervised adversarial learning combines the strengths of semi-supervised learning (utilizing both labeled and unlabeled data) and adversarial training (improving model robustness by incorporating adversarial examples). Current research focuses on enhancing model performance under limited labeled data, employing techniques like generative adversarial networks (GANs), knowledge distillation, and adaptive regularization to improve accuracy and robustness. These methods are applied across diverse domains, including image classification, medical image segmentation, and recommendation systems, demonstrating their potential to address data scarcity challenges and improve the reliability of machine learning models in various applications. The resulting advancements contribute to more efficient and robust AI systems, particularly valuable in scenarios with limited annotated data.