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- \documentclass{beamer}
- \usetheme{metropolis}
- \usepackage{hyperref}
- \usepackage[utf8]{inputenc} % this is needed for german umlauts
- \usepackage[english]{babel} % this is needed for german umlauts
- \usepackage[T1]{fontenc} % this is needed for correct output of umlauts in pdf
- \usepackage{caption}
- \usepackage{tikz}
- \usetikzlibrary{arrows.meta}
- \usetikzlibrary{decorations.pathreplacing}
- \usetikzlibrary{positioning}
- \usetikzlibrary{decorations.text}
- \usetikzlibrary{decorations.pathmorphing}
- \usetikzlibrary{shapes.multipart, calc}
- \usepackage{minted} % needed for the inclusion of source code
- \begin{document}
- \title{Convolutional Neural Networks (CNNs)}
- \subtitle{Theory and Applications}
- \author{Martin Thoma}
- \date{22. February 2019}
- \subject{Machine Learning, AI, Neural Networks, Convolutional Neural Networks}
- \frame{\titlepage}
- % \section{Neural Network Basics}
- % \subsection{}
- \begin{frame}{Artificial Neuron (Perceptron)}
- $$f: \mathbb{R}^n \rightarrow \mathbb{R}$$
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/artificial-neuron.pdf}
- \end{figure}
- % $$f(x) = ax^2 + bx + c \text{ with } f(0) = 3, f(1) = 2, f(-1) = 6$$
- % \begin{align*}
- % \onslide<2->{f(0) &= a \cdot 0^2 + b \cdot 0 + c = 3} &\onslide<3->{\Rightarrow c &= 3\\}
- % \onslide<4->{f(1) &= a \cdot 1^2 + b \cdot 1 + 3 = 2} &\onslide<5->{\Rightarrow a &= -1-b\\}
- % \onslide<6->{f(-1) &= a \cdot {(-1)}^2 - b + 3 = 6\\}
- % \onslide<7->{\Leftrightarrow 3&=a - b\\}
- % \onslide<8->{\Leftrightarrow 3&= (-1-b) - b\\}
- % \onslide<9->{\Leftrightarrow b&= -2\\}
- % \onslide<10>{\Rightarrow \quad f(x) &= x^2 -2 x + 3\\}
- % \end{align*}
- % \only<1>{$$f: \mathbb{R}^n \rightarrow \mathbb{R}^m$$}
- % \only<2>{$$f: \mathbb{R}^2 \rightarrow \mathbb{R}$$
- % # 2x - 1
- % # (x-1)^2 + 1
- % Examples:
- % \begin{itemize}
- % \item $1 \rightarrow 1$: $f(x) = x$
- % \item $2 \rightarrow 3$: $f(x) = $
- % % \item $3 \rightarrow 3$
- % \end{itemize}
- % }
- \end{frame}
- \begin{frame}{Multi-Layer Perceptron (MLP)}
- $$f: \mathbb{R}^n \rightarrow \mathbb{R}^m$$
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/perceptron-notation.pdf}
- \end{figure}
- \end{frame}
- \begin{frame}{}
- \begin{itemize}[<+->]
- \item Predict housing prices: (bed rooms, size, age) $\rightarrow$ Price
- \item Product categorization: (weight, volume, price) $\rightarrow$ \{shoe, handbag, shirt\}
- \item Image classification: List of pixel colors $\rightarrow$ \{cat, dog\}
- \end{itemize}
- \end{frame}
- \begin{frame}{}
- \begin{center}
- \Huge Data
- \end{center}
- \end{frame}
- \begin{frame}{Necessary Data}
- \begin{itemize}
- \item $f(x) = w_0$
- \item $f(x) = w_1 \cdot x + w_0$
- \item $f(x) = w_2^2 \cdot x^2 + w_1^2 \cdot x + w_0$
- \item sin, cos, tan, \dots
- \end{itemize}
- \end{frame}
- \begin{frame}{Convolution}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth]{graphics/convolution-linear.pdf}
- \end{figure}
- \end{frame}
- \begin{frame}{Convolutional Layer}
- \begin{figure}[ht]
- \centering
- \input{graphics/convolution-layer}
- \end{figure}
- \end{frame}
- \begin{frame}{Max Pooling}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth]{graphics/max-pooling.pdf}
- \end{figure}
- \end{frame}
- \section{Applications}
- \begin{frame}{Symbol recognizer}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/symbol-recognizer.png}
- \captionsetup{labelformat=empty}
- \caption{\href{http://write-math.com}{write-math.com}}
- \end{figure}
- \end{frame}
- \begin{frame}{}
- \inputminted[linenos,
- numbersep=7pt,
- gobble=0,
- % frame=none,
- % framesep=2mm,
- fontsize=\footnotesize, tabsize=4]{python}{cnn.py}
- \end{frame}
- \begin{frame}{Super Resolution}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/pixel-recursive-super-resolution.png}
- \captionsetup{labelformat=empty}
- \caption{Dahl, Norouzi, Shlens: Pixel recursive super resolution (2017)}
- \end{figure}
- \end{frame}
- \begin{frame}{Colorization: The Problem}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/multimodality-apple.png}
- \captionsetup{labelformat=empty}
- \caption{Cinarel: Automatic Colorization of Webtoons Using Deep Convolutional Neural Networks (2018)}
- \end{figure}
- Interactive Demo: \href{http://richzhang.github.io/colorization/}{richzhang.github.io/colorization}
- \end{frame}
- \begin{frame}{Colorization - Photographs}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/colorful-image-colorization.png}
- \captionsetup{labelformat=empty}
- \caption{Zhang, Isola, Efros: Colorful Image Colorization (2016)}
- \end{figure}
- Interactive Demo: \href{http://richzhang.github.io/colorization/}{richzhang.github.io/colorization}
- \end{frame}
- \begin{frame}{Colorization - Comic}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/comic-colorization.png}
- \captionsetup{labelformat=empty}
- \caption{Ci, Ma, Wang, Li, Luo: User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks (2018)}
- \end{figure}
- \end{frame}
- \begin{frame}{Denoising}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/denoising.png}
- \captionsetup{labelformat=empty}
- \caption{Zhang, Zuo, Gu, Zhang: Learning Deep CNN Denoiser Prior for Image Restoration (2017)}
- \end{figure}
- \end{frame}
- \begin{frame}{Image Inpainting (Watermark removal)}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/leopard-inpainting.png}
- \captionsetup{labelformat=empty}
- \caption{Yang, Lu, Lin, Shechtman, Wang, Li: High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis (2017)}
- \end{figure}
- \end{frame}
- \begin{frame}{CNNs in NLP}
- \begin{figure}[ht]
- \centering
- \includegraphics[width=0.8\paperwidth, height=0.7\paperheight, keepaspectratio]{graphics/tdnns.png}
- \captionsetup{labelformat=empty}
- \caption{Collobert, Weston, Bottou, Karlen, Kavukcuoglu, Kuksa:
- Natural Language Processing (almost) from Scratch (2011)}
- \end{figure}
- \end{frame}
- \end{document}
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