Model Agnostic Training

Model-agnostic training (MAT) focuses on developing methods to train and improve machine learning models without being tied to a specific architecture or hyperparameter set. Current research emphasizes techniques like automated architecture search, adaptive optimization algorithms that reduce hyperparameter sensitivity, and strategies for leveraging unlabeled or partially labeled data to enhance model robustness and generalization. This approach is significant because it simplifies the training process, improves model efficiency and performance across diverse tasks (e.g., image generation, classification, regression), and reduces the need for extensive hyperparameter tuning and expert knowledge.