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deep-learning-figures's Introduction

Deep Learning Figures

License: CC BY-NC-SA 4.0

These figures have been made mostly during my PhD. Some present general concepts / models of Deep Learning, most are to describe the papers I worked on. The source is a PPTX file containing all the figures. You can try to adapt them to your needs if you feel up for it. If so, I recommend to install the fonts that I used.

These figures are used in my PhD thesis if you want to see them used in context and want a full legend.

How I use PowerPoint figures in my LaTeX documents?

For each paper, I have an images/figures.pptx file that contains all my PowerPoint figures. Regularly, I export this PowerPoint into a images/figures.pdf file. I also have a script images/process_figures.sh and run it after each PDF export:

# Split the PDF into pages
pdfsplit.py figures.pdf
# pdfsplit.py is included in this repo. It is designed for Mac. For Linux or Windows, you can find equivalents.

# Remove pages that I keep in the PPTX but I don't actually want to use
rm figures-3.pdf
rm figures-4.pdf

# Compress some pages if needed, when they contain big images, you need 
compress_pdf () {
    gs -sDEVICE=pdfwrite -dNOPAUSE -dQUIET -dBATCH -dPDFSETTINGS=/printer -dCompatibilityLevel=1.4 -sOutputFile=$1-comp.pdf $1.pdf
    mv $1-comp.pdf $1.pdf
}
compress_pdf figures-1 &
compress_pdf figures-2 &
wait

# Remove the write part of each figure's page
for f in `ls figures-*.pdf`; do
    pdfcrop $f $f  &  # pdfcrop came with my latex install. It's this: https://ctan.org/pkg/pdfcrop
done
wait

# Rename into more usable names
mv figures-1.pdf intro_CV.pdf
mv figures-2.pdf intro_ML.pdf
# ...

General figures

Intro of Computer Vision


Intro of Machine Learning


Intro of Neural Nets


Intro of ConvNets


Intro of Disentangling


Famous ConvNets architectures


VGG architecture by [T. Durand](https://github.co

Illustration of Auto-Encoders


Illustration of Denoising Auto-Encoders


Illustrations of Variational Auto-Encoders



Illustration of GAN


Illustration of Ladder Networks


Goal of the model


Minimizing Entropy


Minimizing Conditional Entropy


Model overview


Intuition


General architecture


Losses


ConvLarge architecture


Example of architecture


Branch balancing effect


Merge strategies



HybridNet with SHADE


Overview


Architecture


Comparison with other models


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