wbj0110 / deep-learning-figures

Figures I made during my PhD in Deep Learning, for my models and for context

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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


SHADE: Information-Based Regularization for Deep Learning

Goal of the model


Minimizing Entropy


Minimizing Conditional Entropy


HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning

Model overview


Intuition


General architecture


Losses


ConvLarge architecture


Example of architecture


Branch balancing effect


Merge strategies



HybridNet with SHADE


DualDis: Dual-Branch Disentangling with Adversarial Learning

Overview


Architecture


Comparison with other models


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Figures I made during my PhD in Deep Learning, for my models and for context

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