CNN Intro

presentations/CNN-Intro/CNN-Intro.tex

@@ -4,13 +4,15 @@
 \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}
 
@@ -107,13 +109,94 @@
 
 \section{Applications}
 \begin{frame}{Symbol recognizer}
-    \begin{center}
-        \href{http://write-math.com}{write-math.com}
-    \end{center}
+\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}{Symbol recognizer}
-GANs
+\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}

presentations/CNN-Intro/Makefile

@@ -1,7 +1,7 @@
 SOURCE = CNN-Intro
 
 make:
-	pdflatex $(SOURCE).tex -output-format=pdf
+	pdflatex -shell-escape $(SOURCE).tex -output-format=pdf
 	make clean
 
 clean:

presentations/CNN-Intro/cnn.py

@@ -0,0 +1,19 @@
+import data
+
+from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+from keras.models import Sequential, load_model
+
+model = Sequential()
+model.add(Conv2D(16, (3, 3)))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Conv2D(16, (3, 3)))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dense(data.n_classes, activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='adam')
+model.fit(data.x_train, data.y_train)
+
+model.save('model.h5')
+model = load_model('model.h5')
+y_predicted = model.predict(data.x_test)