Perceptual Guidance
Perceptual guidance in machine learning aims to improve the realism and quality of generated content, such as images and QR codes, by incorporating human visual perception into model training and generation processes. Current research focuses on integrating perceptual loss functions into diffusion models and employing techniques like classifier-free guidance and perceptual similarity guidance to enhance image editing and generation. This approach leads to more realistic and aesthetically pleasing outputs, impacting diverse applications from improving the scannability of QR codes to enabling prescription-free virtual reality experiences for users with vision impairments. The broader significance lies in bridging the gap between machine-generated content and human expectations, leading to more natural and user-friendly applications of AI.