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Simon Klüttermann 2022-04-01 13:17:20 +02:00
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{
"index":4,
"title":"Pixel Attribution",
"paper":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps",
"paper":"Efficient Saliency Maps for Explainable AI",
"chapter":"10.2",
"by": "Simonyan, Vedaldi, et al.",
"when": "2013",
"by": "Mundhenk, Chen, et al.",
"when": "2019",
"who": "Simon Klüttermann",
"mail": "simon.kluettermann@cs.uni-dortmund.de",
"note1":"Different parts of an image have different effect/importance on the classification of an image",
"note2":"{null}",
"note2":"Programming task: Generate one Saliency Map yourself!",
"ext": "jpeg"
}

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[55] biber:340> INFO - === Fr Apr 1, 2022, 13:12:40
[65] Biber.pm:415> INFO - Reading 'slides.bcf'
[116] Biber.pm:952> INFO - Found 0 citekeys in bib section 0
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\begin{itemize}
\item Slight changes in a neural network can change its output drastically

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\item Represent some model output by well fitting data instances

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\item Use game theory to explain the output of a model
\item You could either focus on cs theory or on medical application

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\begin{frame}
\frametitle{Topic 4: Pixel Attribution}
\Large \textbf{Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps} (Simonyan, Vedaldi, et al., 2013)\\
\Large \textbf{Efficient Saliency Maps for Explainable AI} (Mundhenk, Chen, et al., 2019)\\
\LARGE \textbf{Chapter:} 10.2 \textbf{Supervisor:} Simon Klüttermann (simon.kluettermann@cs.uni-dortmund.de)\\
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\item Different parts of an image have different effect/importance on the classification of an image
\item Programming task: Generate one Saliency Map yourself!
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