Training Pair

Training pairs, consisting of input and corresponding output data, are fundamental to many machine learning tasks, but often suffer from misalignment or other imperfections in real-world scenarios. Current research focuses on developing robust training methodologies that address these issues, employing techniques like reblurring, deformation-aware GANs, and adaptive weighting of training samples to improve model accuracy and generalization. These advancements are crucial for improving the performance of various applications, including image restoration, medical image synthesis, and more generally, enhancing the reliability of models trained on imperfect or noisy data.