Présentation Sorbonne Université

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\documentclass[compress,xcolor=table]{beamer}

% Packages
\usepackage[english]{babel}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{datetime}

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\usetheme[compressminiframes]{sorbonne}

% Title page
\title{Lorem Ipsum \newline Dolor Sit Amet}
\foottitle{Lorem Ipsum Dolor Sit Amet} % optional, printed at the bottom of the slides, by default same as title, can be useful to rewrite when title has a newline for example
\subtitle{Subtitle} % optional subtitle
\date{\formatdate{22}{03}{2018}}
\author{Prénom Nom}
% \institute{LIP6 - Sorbonne Université} % Optional

% Biblatex
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%% BEGIN OF SLIDES
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\begin{document}

\begin{frame}[plain]
	\titlepage
	\setcounter{framenumber}{0}
\end{frame}


\section{Noise filtering in UPMC Food-101} \subsection{}

\begin{frame}{Principle}
	
	\begin{alertblock}{The problem}
		
		\begin{itemize}
			\item
			UPMC Food-101 has been crawled from Google Images
			\item
			It contains a certain amount of noise
		\end{itemize}
		
	\end{alertblock}
	
	\begin{block}{The idea}
		
		\begin{itemize}
			\item
			Creating bags from images of 1 class (e.g.~pizza)
			\item
			Creating bags for ``rest'' class
			\item
			Defining the expected level of noise in the pizza bags
			\item
			Using \emph{Learning with Label Proportions} models to detect noise
		\end{itemize}
		
	\end{block}
	
\end{frame}

\begin{frame}{Experimentations}
	
	\begin{block}{Protocol}
		
		\begin{itemize}
			\item
			\textbf{Dataset:}
			\((x_i, y_i, y_i^*) \in \mathbb{R}^p \times \{-1,1\} \times \{-1,1\},\quad i=1..n\)
			
			\begin{itemize}
				\item
				\(x_i\) features, \(y_i\) noisy label, \(y_i^*\) true label (not
				available for training)
			\end{itemize}
			\item
			\textbf{Create bags:} Create bags \(b_j\) of 30 points having the same
			\(y_i\) and give them a proportion of positive points \(p_j\)
			\item
			\textbf{Training:} Train the SyMIL model on bags and train an SVM on
			\((b_j, p_j)\) / \((x_i, y_i)\)
			\item
			\textbf{Evaluation:} Use the decision frontiers of SyMIL / SVM models
			to reclassify each \(x_i\)s by predicting \(\hat{y}_i^*\), and compare
			with \(y_i^*\)
		\end{itemize}
		
	\end{block}
	
\end{frame}

\begin{frame}{Some papers for noisy images datasets}
	
	\begin{exampleblock}{\citetitle{Azadi2015} \cite{Azadi2015}}
		
		\begin{itemize}
			\item
			Define a regularized loss for training the CNN
			\item
			Can be seen as looking for the label of similar images for
			regularization
			\item
			Results slightly better than Sukhbaatar model
		\end{itemize}
		
	\end{exampleblock}
	
\end{frame}

\section{References} \subsection{}

\begin{frame}[allowframebreaks]{References}
	
	\printbibliography[heading=none]
	
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\end{document}