BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241016T150000
DTEND;TZID=America/New_York:20241016T160000
DTSTAMP:20260603T030535
CREATED:20241010T152537Z
LAST-MODIFIED:20241010T152537Z
UID:12359-1729090800-1729094400@seasevents.nmsdev7.com
SUMMARY:Fall 2024 GRASP SFI: Jun-Yan Zhu\, Carnegie Mellon University\, "Ensuring Data Ownership in Generative Visual Models"
DESCRIPTION:This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. \nABSTRACT\nLarge-scale generative visual models have made content creation as little effort as writing a short text description. However\, these models are typically trained on an enormous amount of Internet data\, often containing copyrighted material\, licensed images\, and personal photos. How can we remove these images if creators decide to opt out? How can we properly compensate them if they choose to opt in? \nIn this talk\, I will first describe an efficient method for removing copyrighted materials\, artistic styles of living artists\, and memorized images from pretrained text-to-image models. I will then discuss our data attribution algorithms for assessing the influence of each training image for a generated sample. Collectively\, we aim to enable creators to retain control over the ownership of training images.
URL:https://seasevents.nmsdev7.com/event/fall-2024-grasp-sfi-jun-yan-zhu/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
END:VCALENDAR