BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:W. Europe Standard Time BEGIN:STANDARD DTSTART:16011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20220509T063252Z DESCRIPTION:Diffusion Models Beat GANs on Image Synthesis\n\nDate: May 24th 2022\nLinks: https://proceedings.neurips.cc/paper/2021/file/49ad23d1ec9fa 4bd8d77d02681df5cfa-Paper.pdf\nAbstract:\nDenoising diffusion probabilisti c models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. After an introduction to DDPM\, we sho w that diffusion models can achieve image sample quality superior to the c urrent state-of-the-art generative models. We achieve this first by improv ing model architecture that give a substantial boost to Fréchet inception distance (FID) and then by devising a scheme for trading off diversity fo r fidelity.\nSpeakers: Helmand Shayan (TH OWL)\n \n DTEND;TZID="W. Europe Standard Time":20220524T180000 DTSTAMP:20220509T063252Z DTSTART;TZID="W. Europe Standard Time":20220524T160000 LAST-MODIFIED:20220509T063252Z LOCATION:https://th-owl.webex.com/th-owl/j.php?MTID=mbbde0e6c66879d20d7faee 10d379af14 PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=de:Machine Learning Reading Group Round 8 TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E008000000008020FEFF0662D801000000000000000 0100000002367160CB5945144A28456711293B678 X-ALT-DESC;FMTTYPE=text/html:

Diffusion Models Beat GANs on Image Synthesi s

Date: May 24th 2022

Links: https://proceedings.neurips.cc/paper/2021 /file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf

Abstract:

Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. After an introduction to DDPM\, we show that diffusion models can ach ieve image sample qual ity superior to the current state-of-the -art generative models. We achieve this first by improving model architecture that g ive a substantial boost to Fréchet inceptio n distance (FID) and then by devising a scheme for trading off diversity for fidelit y.

Speakers: Helmand Shay an (TH OWL)

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