Perceptual Loss

Perceptual loss functions aim to improve the quality of generated or restored data (images, audio, video) by incorporating measures of human perception into the training process of machine learning models, rather than relying solely on pixel-wise or other objective metrics. Current research focuses on developing and refining these loss functions, often integrating them with generative adversarial networks (GANs), diffusion models, and transformers, and exploring optimal combinations of perceptual and distortion losses to achieve a balance between fidelity and realism. This work is significant because it leads to more natural and visually appealing outputs in various applications, including image super-resolution, speech enhancement, and medical image processing, ultimately improving the performance and usability of these technologies.