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\documentclass[t]{beamer} | ||
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\title{Comparative Evaluation of Incremental Machine Learning} | ||
\subtitle{University of Applied Sciences Fulda} | ||
% % Handouts | ||
% \documentclass[handout,t]{beamer} | ||
% \usepackage{pgfpages} | ||
% \pgfpagesuselayout{4 on 1}[a4paper,border shrink=5mm,landscape] | ||
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\usetheme[ | ||
faculty=phil, | ||
logo=title_university | ||
]{fibeamer} | ||
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\usepackage{graphicx} | ||
\graphicspath{{./resources/images/}} | ||
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\usepackage[style=alphabetic,sorting=none]{biblatex} | ||
\addbibresource{references.bib} | ||
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\usepackage{tikz} | ||
\usetikzlibrary{matrix,chains,positioning,decorations.pathreplacing,arrows} | ||
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\title{Comparative Evaluation of\\Incremental Machine Learning} | ||
\subtitle{Colloquium} | ||
\author{Florian Wiech} | ||
\institute{University of Applied Sciences Fulda} | ||
\date{\today} | ||
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\begin{document} | ||
\frame { | ||
\titlepage | ||
} | ||
\frame{ | ||
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\begin{frame} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{agi} | ||
\caption{\cite{agi} AGI - Artificial General Intelligence} | ||
\label{fig:agi} | ||
\end{figure} | ||
\end{frame} | ||
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\begin{frame}[c] | ||
\titlepage | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{Table of Contents} | ||
\framesubtitle{} | ||
\begin{itemize} | ||
\item … | ||
\end{itemize} | ||
} | ||
\frame{ | ||
\frametitle{Title} | ||
… | ||
} | ||
\tableofcontents | ||
\end{frame} | ||
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\include{sections/1_Introduction} | ||
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\include{sections/2_EWC} | ||
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\include{sections/3_Conclusion_Outlook} | ||
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\begin{frame}[c] | ||
\centering \Huge | ||
Thank You! | ||
\end{frame} | ||
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% \section{References} | ||
\begin{frame}[allowframebreaks] | ||
\frametitle{References} | ||
\printbibliography | ||
\end{frame} | ||
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\end{document} |
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@misc{agi, | ||
url = {https://www.topbots.com/wp-content/uploads/2017/09/agi_out_of_reach_1600px_web-1280x640.jpg}, | ||
date = {2017-09-04}, | ||
author = {Mariya Yao}, | ||
title = {Depite AI hype, true general intelligence still out of reach}, | ||
note = {Figure \ref{fig:agi}; Online; accessed: 2019-05-10} | ||
} | ||
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@misc{dnn_neuron_basic_overview, | ||
url = {https://tex.stackexchange.com/questions/132444/diagram-of-an-artificial-neural-network}, | ||
date = {2019-03-01}, | ||
author = {Gonzalo Medina}, | ||
title = {Diagram of an artificial neural network} | ||
} |
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\section{Introduction} | ||
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\subsection{Catastrophic Forgetting in Neural Networks} | ||
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\begin{frame} | ||
\frametitle{Neural Network} | ||
\framesubtitle{Training} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{training_preview} | ||
\end{figure} | ||
\footnotetext{\cite{dnn_neuron_basic_overview}} | ||
\end{frame} | ||
\begin{frame} | ||
\frametitle{Neural Network} | ||
\framesubtitle{Training} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{training_result} | ||
\end{figure} | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{Neural Network} | ||
\framesubtitle{Retraining} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{retraining_preview} | ||
\end{figure} | ||
\end{frame} | ||
\begin{frame} | ||
\frametitle{Neural Network} | ||
\framesubtitle{Retraining} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{retraining_result} | ||
\end{figure} | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{Catastrophic Forgetting} | ||
\framesubtitle{Timeline} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{D55_catastrophic_forgetting} | ||
\caption{training (T1) \& retraining (T2)} | ||
\label{fig:CF_D55} | ||
\end{figure} | ||
\end{frame} | ||
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\subsection{Importance of Catastrophic Forgetting} | ||
\begin{frame} | ||
\frametitle{Catastrophic Forgetting} | ||
\framesubtitle{Why is it important?} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{cf_importance} | ||
\label{fig:cf_importance} | ||
\end{figure} | ||
\end{frame} | ||
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\subsection{Related Work} | ||
\begin{frame} | ||
\frametitle{Related Work} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{related_work} | ||
\label{fig:related_work} | ||
\end{figure} | ||
\end{frame} |
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\section{Algorithm evaluation} | ||
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\subsection{Explanation} | ||
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\begin{frame} | ||
\frametitle{EWC} | ||
\framesubtitle{Elastic Weight Consolidation} | ||
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\begin{columns}[onlytextwidth] | ||
\column{.5\textwidth} | ||
\begin{itemize} | ||
\item protects previous \\gained knowledge | ||
\item classifies importance \\of each thread | ||
\item adaption of less important threads at re-training | ||
\item protection discovery with Fisher Information matrix | ||
\end{itemize} | ||
\column{.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{ewc} | ||
\label{fig:ewc_expl} | ||
\end{figure} | ||
\end{columns} | ||
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\end{frame} | ||
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\subsection{Review \& Improvements} | ||
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\begin{frame} | ||
\frametitle{EWC} | ||
\framesubtitle{Review} | ||
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\begin{columns}[onlytextwidth] | ||
\column{.5\textwidth} | ||
\begin{itemize} | ||
\item unproven assumptions | ||
\item implementation problem\\with TensorFlow | ||
\end{itemize} | ||
\column{.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{ewc} | ||
\end{figure} | ||
\end{columns} | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{EWC} | ||
\framesubtitle{Improvements} | ||
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\begin{columns}[onlytextwidth] | ||
\column{.5\textwidth} | ||
\begin{itemize} | ||
\item replace FIM with new matrix (Gradient matrix) | ||
\item less assumptions necessary | ||
\item solves implementation problem in TensorFlow | ||
\end{itemize} | ||
\column{.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{ewc_improvement} | ||
\label{fig:ewc_improve} | ||
\end{figure} | ||
\end{columns} | ||
\end{frame} | ||
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\subsection{Benchmarks} | ||
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\begin{frame} | ||
\frametitle{Approach} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{approach} | ||
\end{figure} | ||
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% \begin{enumerate} | ||
% \item Original EWC implementation | ||
% \item Propriety validation | ||
% \item Replacement of FIM with GM | ||
% \item Comparison of modification and implementation | ||
% \end{enumerate} | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{Benchmark} | ||
\framesubtitle{Disjoint D9-1} | ||
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\begin{itemize} | ||
\item T1: 9 classes; T2: 1 class | ||
\item discovers the capability of a re-training with small datasets | ||
\item approximately 10\% added | ||
\end{itemize} | ||
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\begin{columns} | ||
\column{0.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{project/baseline/D91_FIM} | ||
\caption{EWC-FIM D9-1} | ||
\label{fig:ewc_fim_d91} | ||
\end{figure} | ||
\column{0.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{project/experiments/D91_GM} | ||
\caption{EWC-GM D9-1} | ||
\label{fig:ewc_gm_d91} | ||
\end{figure} | ||
\end{columns} | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{Benchmark} | ||
\framesubtitle{Disjoint D5-5} | ||
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\begin{itemize} | ||
\item T1: 5 classes; T2: 5 classes | ||
\item challenge to handle the same data volume | ||
\end{itemize} | ||
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\begin{columns} | ||
\column{0.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{project/baseline/D55_FIM} | ||
\caption{EWC-FIM D5-5} | ||
\label{fig:ewc_fim_d55} | ||
\end{figure} | ||
\column{0.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{project/experiments/D55_GM} | ||
\caption{EWC-GM D5-5} | ||
\label{fig:ewc_gm_d55} | ||
\end{figure} | ||
\end{columns} | ||
\end{frame} | ||
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\begin{frame} | ||
\frametitle{Benchmark} | ||
\framesubtitle{Permuted P10-10} | ||
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\begin{itemize} | ||
\item T1: 10 classes; T2: permuted classes | ||
\item challenge to learn different data-chunks with the same classes | ||
\end{itemize} | ||
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\begin{columns} | ||
\column{0.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{project/baseline/PM_FIM} | ||
\caption{EWC-FIM P10-10} | ||
\label{fig:ewc_fim_pm} | ||
\end{figure} | ||
\column{0.5\textwidth} | ||
\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{project/experiments/PM_GM} | ||
\caption{EWC-GM P10-10} | ||
\label{fig:ewc_gm_pm} | ||
\end{figure} | ||
\end{columns} | ||
\end{frame} |
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\section{Conclusion \& Outlook} | ||
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\begin{frame} | ||
\frametitle{Conclusion \& Outlook} | ||
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\begin{itemize} | ||
\item Gradient matrix is a possible simplification | ||
\item Fixed implementation issue | ||
\item EWC not ready for deployment | ||
\end{itemize} | ||
\pause | ||
\begin{itemize} | ||
\item Further testing | ||
\begin{itemize} | ||
\item different networks | ||
\item different datasets | ||
\end{itemize} | ||
\item Further integrations | ||
\begin{itemize} | ||
\item integration of GM in different algorithms | ||
\end{itemize} | ||
\end{itemize} | ||
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\end{frame} | ||
\begin{frame} | ||
\frametitle{Outlook} | ||
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\begin{figure}[H] | ||
\centering | ||
\includegraphics[width=\textwidth]{agi} | ||
\end{figure} | ||
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\pause | ||
\centering \LARGE | ||
Not quite there yet… | ||
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\end{frame} |