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reworked deepsad procedure diagram

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This commit is contained in:
Jan Kowalczyk
2025-09-28 16:12:24 +02:00
parent d93f1a52a9
commit 1e71600102
4 changed files with 18 additions and 18 deletions
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35
thesis/diagrams/deepsad_procedure/deepsad_procedure.tex
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@@ -1,5 +1,6 @@
\documentclass[tikz,border=10pt]{standalone}
\usepackage{tikz}
\usepackage{amsfonts}
\usetikzlibrary{positioning, shapes.geometric, fit, arrows, arrows.meta, backgrounds}
% Define box styles
@@ -7,9 +8,9 @@
databox/.style={rectangle, align=center, draw=black, fill=blue!50, thick, rounded corners},%, inner sep=4},
procbox/.style={rectangle, align=center, draw=black, fill=orange!30, thick, rounded corners},
hyperbox/.style={rectangle, align=center, draw=black, fill=green!30, thick, rounded corners},
stepsbox/.style={rectangle, align=left, draw=black,fill=white, rounded corners, minimum width=6cm, minimum height=1.5cm, font=\small},
outputbox/.style={rectangle, align=center, draw=red!80, fill=red!20, rounded corners, minimum width=6cm, minimum height=1.5cm, font=\small},
hlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=6cm, minimum height=1.5cm, font=\small},
stepsbox/.style={rectangle, align=left, draw=black,fill=white, rounded corners, minimum width=5.2cm, minimum height=1.5cm, font=\small},
outputbox/.style={rectangle, align=center, draw=red!80, fill=red!20, rounded corners, minimum width=5.2cm, minimum height=1.5cm, font=\small},
hlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=5.2cm, minimum height=1.5cm, font=\small},
vlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=3cm, minimum height=1.8cm, font=\small},
arrow/.style={-{Latex[length=3mm]}},
arrowlabel/.style={fill=white,inner sep=2pt,midway}
@@ -25,11 +26,11 @@
\begin{tikzpicture}[node distance=1cm and 2cm]
\node (data) {Data};
\node[right=7 of data] (process) {Procedure};
\node[right=7 of process] (hyper) {Hyperparameters};
\node[right=4.9 of data] (process) {Procedure};
\node[right=4.1 of process] (hyper) {Hyperparameters};
\begin{pgfonlayer}{foreground}
\node[hlabelbox, below=of data] (unlabeled) {\boxtitle{Unlabeled Data} More normal than \\ anomalous samples required};
\node[hlabelbox, below=1.29 of data] (unlabeled) {\boxtitle{Unlabeled Data} Significantly more normal than \\ anomalous samples required};
\node[hlabelbox, below=.1 of unlabeled] (labeled) {\boxtitle{Labeled Data} No requirement regarding ratio \\ +1 = normal, -1 = anomalous};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
@@ -39,16 +40,16 @@
%\draw[arrow] (latent.east) -- node{} (autoenc.west);
\begin{pgfonlayer}{foreground}
\node[stepsbox, below=of process] (pretrainproc) {Train Autoencoder for $E_A$ Epochs \\ with $L_A$ Learning Rate \\ No Labels Used};
\node[outputbox, below=.1 of pretrainproc] (pretrainout) {\boxtitle{Outputs} Encoder Network \\ $\mathbf{w}$: Network Weights};
\node[stepsbox, below=of process] (pretrainproc) {Train Autoencoder $\mathcal{W}_{E}$ \\ optimize Autoencoding Objective \\ for $E_A$ Epochs \\ with $L_A$ Learning Rate \\ No Labels Used / Required};
\node[outputbox, below=.1 of pretrainproc] (pretrainout) {\boxtitle{Outputs} $\mathcal{W}$: Encoder / DeepSAD Network \\ $\mathbf{w_{E}}$: Encoder Network Weights};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
\node[procbox, fit=(pretrainproc) (pretrainout), label={[label distance = 1, name=pretrainlab]above:{\textbf{Pre-Training of Autoencoder}}}] (pretrain) {};
\end{pgfonlayer}
\begin{pgfonlayer}{foreground}
\node[hlabelbox, below=of hyper] (autoencarch) {\boxtitle{Autoencoder Architecture} Choose based on data type \\ Latent Space Size (based on complexity)};
\node[hlabelbox, below=.1 of autoencarch] (pretrainhyper) {\boxtitle{Hyperparameters} $E_A$: Number of Epochs \\ $L_A$: Learning Rate};
\node[hlabelbox, below=1.26 of hyper] (autoencarch) {\boxtitle{Autoencoder Architecture} $\mathcal{W}_{E}$: Autoencoder Network \\ $\mathbb{R}^d$: Latent Space Size };
\node[hlabelbox, below=.1 of autoencarch] (pretrainhyper) {\boxtitle{Hyperparameters} $E_A$: Number of Epochs \\ $L_A$: Learning Rate AE};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
\node[hyperbox, fit=(autoencarch) (pretrainhyper), label={[label distance = 1, name=autoenclabel]above:{\textbf{Pre-Training Hyperparameters}}}] (pretrainhyp) {};
@@ -61,7 +62,7 @@
% \draw[arrow] (node cs:name=autoenc,angle=196) |- (node cs:name=pretrain,angle=5);
\begin{pgfonlayer}{foreground}
\node[stepsbox, below=1.4 of pretrain] (calccproc) {1. Init Encoder with $\mathbf{w}$ \\ 2. Forward Pass on all data \\ 3. $\mathbf{c}$ = Mean Latent Representation};
\node[stepsbox, below=1.4 of pretrain] (calccproc) {Init Network $\mathcal{W}$ with $\mathbf{w_{E}}$ \\ Forward Pass on all data \\ Hypersphere center $\mathbf{c}$ is mean \\ of all Latent Representation};
\node[outputbox, below=.1 of calccproc] (calccout) {\boxtitle{Outputs} $\mathbf{c}$: Hypersphere Center};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
@@ -76,21 +77,21 @@
%\draw[arrow] (node cs:name=traindata,angle=-45) |- node[arrowlabel]{all training data, labels removed} (node cs:name=calcc,angle=200);
\begin{pgfonlayer}{foreground}
\node[stepsbox, below=1.4 of calcc] (maintrainproc) {Train Network for $E_M$ Epochs \\ with $L_M$ Learning Rate \\ Considers Labels with $\eta$ strength};
\node[outputbox, below=.1 of maintrainproc] (maintrainout) {\boxtitle{Outputs} Encoder Network \\ $\mathbf{w}$: Network Weights \\ $\mathbf{c}$: Hypersphere Center};
\node[stepsbox, below=1.4 of calcc] (maintrainproc) {Init Network $\mathcal{W}$ with $\mathbf{w_{E}}$ \\ Train Network $\mathcal{W}$ \\ optimize DeepSAD Objective\\ for $E_M$ Epochs \\ with $L_M$ Learning Rate \\ Considers Labels with $\eta$ strength};
\node[outputbox, below=.1 of maintrainproc] (maintrainout) {\boxtitle{Outputs} $\mathcal{W}$: DeepSAD Network \\ $\mathbf{w}$: DeepSAD Network Weights \\ $\mathbf{c}$: Hypersphere Center};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
\node[procbox, fit=(maintrainproc) (maintrainout), label={[label distance = 1, name=maintrainlab]above:{\textbf{Main Training}}}] (maintrain) {};
\end{pgfonlayer}
\begin{pgfonlayer}{foreground}
\node[hlabelbox, below=11.25 of hyper] (maintrainhyper) {$E_M$: Number of Epochs \\ $L_M$: Learning Rate \\ $\eta$: Strength Labeled/Unlabeled};
\node[hlabelbox, below=12.48 of hyper] (maintrainhyper) {$E_M$: Number of Epochs \\ $L_M$: Learning Rate \\ $\eta$: Weight Labeled/Unlabeled};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
\node[hyperbox, fit=(maintrainhyper), label={[label distance = 1, name=autoenclabel]above:{\textbf{Main-Training Hyperparameters}}}] (maintrainhyp) {};
\end{pgfonlayer}
\draw[arrow] (node cs:name=pretrain,angle=-20) -- +(1, 0) |- (node cs:name=maintrain,angle=20);
\draw[arrow] (node cs:name=pretrain,angle=-50) |- +(1.5, -0.55) -- +(1.5,-5.4) -| (node cs:name=maintrain,angle=50);
%\draw[arrow] (pretrainoutput.south) -- (node cs:name=maintrain,angle=22);
@@ -101,7 +102,7 @@
\begin{pgfonlayer}{foreground}
\node[stepsbox, below=1.4 of maintrain] (inferenceproc) {Forward Pass through Network = $\mathbf{p}$ \\ Calculate Geometric Distance $\mathbf{p} \rightarrow \mathbf{c}$ \\ Anomaly Score = Geometric Distance};
\node[stepsbox, below=1.4 of maintrain] (inferenceproc) {Init Network $\mathcal{W}$ with $\mathbf{w}$ \\Forward Pass on sample = $\mathbf{p}$ \\ Calculate Distance $\mathbf{p} \rightarrow \mathbf{c}$ \\ Distance = Anomaly Score};
\node[outputbox, below=.1 of inferenceproc] (inferenceout) {\boxtitle{Outputs} Anomaly Score (Analog Value) \\ Higher for Anomalies};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
@@ -109,7 +110,7 @@
\end{pgfonlayer}
\begin{pgfonlayer}{foreground}
\node[hlabelbox, below=11.8 of traindata] (newdatasample) {\boxtitle{New Data Sample} Same data type as training data};
\node[hlabelbox, below=13.32 of traindata] (newdatasample) {\boxtitle{New Data Sample} Same data type as training data};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
\node[databox, fit=(newdatasample), label={[label distance = 1] above:{\textbf{Unseen Data}}}] (newdata) {};