Reciprocal Learning

Reciprocal learning is a machine learning paradigm where models iteratively refine both their parameters and training data, leading to improved performance. Current research focuses on applying this framework to diverse tasks, including speaker identification (using neural networks and techniques like Speaker Reciprocal Points Learning), few-shot class-incremental learning (employing hyperbolic geometry and metric learning), and medical image segmentation (leveraging adversarial training). This approach shows promise in enhancing model accuracy and robustness across various domains, particularly in scenarios with limited data or noisy information.