A new approach to evaluating dense prediction tasks is introduced, focusing on a loss function that better preserves structural similarity in predictions. Traditional pixel-wise error measurements can misrepresent performance, especially with subtle transformations between images. By training a neural network to predict these transformations, the proposed method aims to enhance learning accuracy and improve the assessment of image relationships.
