Training Based Model Refinement

Training-based model refinement focuses on improving the performance and robustness of pre-trained models by iteratively adjusting their parameters using additional data or strategies. Current research emphasizes techniques like importance sampling to optimize training distributions, adversarial training to correct misclassifications, and the integration of pre-trained models as feature extractors or refinement modules within larger architectures (e.g., U-Net, ResNet). These advancements are significant for various applications, including autonomous driving, image restoration, and natural language processing, by enhancing model accuracy, efficiency, and privacy while mitigating issues like error accumulation and sensitivity to outliers.