forked from lukas/Seminar_in_AI
reorder some slides
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@@ -61,38 +61,23 @@
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% SECTION 1: INTRODUCTION
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%----------------------------------------------------------------------------------------
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\section{Introduction}
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\begin{frame}{Goal of the Paper}
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\begin{itemize}
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\item Scatterplots are fundamental for exploring multidimensional data
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\item But: with large datasets they suffer from \textbf{overplotting}
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\item Dense regions obscure structure, samples become inaccessible
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\item Goal:
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\begin{itemize}
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\item Reduce clutter
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\item Preserve neighborhood relations
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\item Achieve uniform sample distribution
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\item Maintain interpretability
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\end{itemize}
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\end{itemize}
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\end{frame}
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%----------------------------------------------------------------------------------------
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% SECTION 2: PROBLEM
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%----------------------------------------------------------------------------------------
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\section{Problem: Overplotting}
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\section{Introduction}
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\begin{frame}{Why Scatterplots Clutter}
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\begin{frame}{Problem: Scatterplots Clutter}
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\begin{itemize}
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\item Scatterplots are fundamental for exploring multidimensional data
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\item Modern datasets: millions of samples
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\item Pixel resolution fixed → many samples map to the same pixel
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\item This results in \textbf{overplotting}
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\item Consequences:
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\begin{itemize}
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\item Occlusion of clusters + outliers
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\item Occlusion of clusters
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\item Loss of density information
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\item Hard to select individual items
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\item Hard to select and see individual items
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%\item Misleading visual perception
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\end{itemize}
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\item A method is needed to \textbf{declutter} without losing structure
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@@ -105,6 +90,18 @@
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\footnotesize\text{Source: \cite{statisticsglobe_overplotting_r}}
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\end{frame}
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\begin{frame}{Goal of the Paper}
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\begin{itemize}
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\item Goal:
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\begin{itemize}
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\item Reduce clutter
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\item Preserve neighborhood relations
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\item Achieve uniform sample distribution
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\item Maintain interpretability
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}{Limitations of Traditional Approaches}
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\begin{itemize}
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\item Transparency-based methods
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@@ -124,12 +121,11 @@
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\item Need a \textbf{global}, \textbf{smooth}, \textbf{monotonic}, \textbf{collision-free} method
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\end{itemize}
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\end{frame}
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%----------------------------------------------------------------------------------------
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% SECTION 3: BACKGROUND
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%----------------------------------------------------------------------------------------
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\section{Background: Density Fields \& Integral Images}
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\section{Background:\\Density Fields \& Integral Images}
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\begin{frame}{Density Estimation}
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\begin{itemize}
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@@ -400,21 +396,6 @@
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\includegraphics[scale=0.4]{rsc/img}
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\end{frame}
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% --- THE END
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\begin{frame}[focus]
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Thanks for your Attention!
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\end{frame}
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%----------------------------------------------------------------------------------------
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% CLOSING/SUPPLEMENTARY SLIDES
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%----------------------------------------------------------------------------------------
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\appendix
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\section{Backup}\label{sec:backup}
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\begin{frame}{Example I}
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\centering
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\includegraphics[scale=0.1]{rsc/2408.06513v1_page_8_1}\\
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@@ -451,6 +432,40 @@
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\footnotesize\text{Source: \cite{Rave_2025}}
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\end{frame}
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% --- THE END
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\begin{frame}[focus]
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Thanks for your Attention!
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\end{frame}
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%----------------------------------------------------------------------------------------
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% CLOSING/SUPPLEMENTARY SLIDES
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%----------------------------------------------------------------------------------------
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\appendix
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\section{Backup}\label{sec:backup}
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\begin{frame}{Domain Transformation (Molchanov \& Linsen)}
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\begin{itemize}
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\item Integral Images $\rightarrow$ Transformation mapping
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\item Definition:
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\[
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t(x,y; d) = \frac{
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\alpha q_1 + \beta q_2 + \gamma q_3 + \delta q_4
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+ \alpha_t (x,1) + \beta_t (1,y) + \gamma_t (x,0) + \delta_t (0,y)
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}{2C}
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\]
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\item Problems:
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\begin{itemize}
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\item Not identity for uniform density
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\item Iteration unstable
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\item Does not converge to equalized distribution
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\end{itemize}
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\end{itemize}
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\end{frame}
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\begin{frame}{Sources}
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\nocite{*} % Display all references regardless of if they were cited
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\bibliography{sources}
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