BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UC Irvine//CML Seminars//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:From Heatmaps to Structural and Counterfactual Explanations
X-WR-TIMEZONE:America/Los_Angeles
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:2024-01-08-fuxin-li@cml.ics.uci.edu
DTSTAMP:20240108T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240108T130000
DTEND;TZID=America/Los_Angeles:20240108T140000
SUMMARY:[CML Seminar] Fuxin Li: From Heatmaps to Structural and Counterfact
ual Explanations
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Fuxin Li\, Associate Professor of Electrical Engineering and Co
mputer Science\, Oregon State University\n\nTitle: From Heatmaps to Struct
ural and Counterfactual Explanations\n\nAbstract: This talk will focus on
our endeavors in the past few years on explaining deep image models. Reali
zing that an important missing piece for explaining neural networks is a r
eliable heatmap visualization tool\, we developed I-GOS and iGOS++ which o
ptimize with integrated gradients to avoid local optima in heatmap generat
ions and improve performance in high-resolution heatmaps. During the devel
opment of those visualizations\, we realize that for a significant number
of images\, the classifier has multiple different paths to reach a confide
nt prediction. This leads to our recent development of structural attentio
n graphs\, an approach that utilizes beam search to locate multiple coarse
heatmaps for a single image. Finally\, we present results traversing the
latent space of variational autoencoders and generative adversarial networ
ks (GANs)\, generating high-quality counterfactual explanations that visua
lly show how to change one image so that CNNs predict them as another cate
gory.\n\nhttps://cml.ics.uci.edu/seminars/2024-01-08-fuxin-li
X-ALT-DESC;FMTTYPE=text/html:Fuxin Li\, Associate Profes
sor of Electrical Engineering and Computer Science\, Oregon State Universi
ty
Title: From Heatmaps to Structural and Counterfactual Exp
lanations
Abstract: This talk will focus on our endeavors in
the past few years on explaining deep image models. Realizing that an imp
ortant missing piece for explaining neural networks is a reliable heatmap
visualization tool\, we developed I-GOS and iGOS++ which optimize with int
egrated gradients to avoid local optima in heatmap generations and improve
performance in high-resolution heatmaps. During the development of those
visualizations\, we realize that for a significant number of images\, the
classifier has multiple different paths to reach a confident prediction. T
his leads to our recent development of structural attention graphs\, an ap
proach that utilizes beam search to locate multiple coarse heatmaps for a
single image. Finally\, we present results traversing the latent space of
variational autoencoders and generative adversarial networks (GANs)\, gene
rating high-quality counterfactual explanations that visually show how to
change one image so that CNNs predict them as another category.
https://cml.ic
s.uci.edu/seminars/2024-01-08-fuxin-li
URL:https://cml.ics.uci.edu/seminars/2024-01-08-fuxin-li
END:VEVENT
END:VCALENDAR