{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"Why Diffusion Models Work So Well — And Where They Break","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/5c3a7945\"></iframe>","width":"100%","height":180,"duration":577,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/why-diffusion-models-work-so-well-and-where-they-break.\n             This is a Plain English Papers summary of a research paper called Elucidating the SNR-t Bias of Diffusion Probabilistic Models [https://www.aimodels.fyi/pape... \n            Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #artificial-intelligence, #data-science, #design, #diffusion-models, #snr-t-bias, #diffusion-inference, #signal-to-noise-ratio, #wavelet-domain,  and more.\n            \n            \n            This story was written by: @aimodels44. Learn more about this writer by checking @aimodels44's about page,\n            and for more stories, please visit hackernoon.com.\n            \n                \n                \n                Diffusion models hide a training-inference mismatch that hurts detail and sharpness. This article explains the problem and the fix.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}