This Paper from Cornell Introduces Multivariate Learned Adaptive Noise (MuLAN): Advancing Machine Learning in Image Synthesis with Enhanced Diffusion Models

Diffusion models stand out for their ability to create high-quality images by transforming data into noise, a process inspired by thermodynamics. This transformation, central to the performance of these models, has become a key area of study in generative modeling and image synthesis, especially for its potential to enhance image quality through novel methodologies. The […]

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