\documentclass[tikz,border=10pt]{standalone}
\usepackage{tikz}
\usepackage{amsfonts}
\usetikzlibrary{positioning, shapes.geometric, fit, arrows, arrows.meta, backgrounds}

% Define box styles
\tikzset{
  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=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}
}

\newcommand{\boxtitle}[1]{\textbf{#1}\\[.4em]}

\pgfdeclarelayer{background}
\pgfdeclarelayer{foreground}
\pgfsetlayers{background,main,foreground}

\begin{document}
\begin{tikzpicture}[node distance=1cm and 2cm]

	\node (data) {Data};
	\node[right=4.9 of data] (process) {Procedure};
	\node[right=4.1 of process] (hyper) {Hyperparameters};

	\begin{pgfonlayer}{foreground}
		\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}
		\node[databox, fit=(unlabeled) (labeled), label={[label distance = 1, name=traindatalabel]above:{\textbf{Training Data}}}] (traindata) {};
	\end{pgfonlayer}

	%\draw[arrow] (latent.east) -- node{} (autoenc.west);

	\begin{pgfonlayer}{foreground}
		\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=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) {};
	\end{pgfonlayer}

	\draw[arrow] (pretrainhyp.west) -- (pretrain.east);

	%\draw[arrow] (node cs:name=traindata,angle=10) -- node[arrowlabel]{data type} (node cs:name=autoenc,angle=177);

	%	\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) {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}
		\node[procbox, fit=(calccproc) (calccout), label={[label distance = 1, name=calcclab]above:{\textbf{Calculate Hypersphere Center}}}] (calcc) {};
	\end{pgfonlayer}

	\draw[arrow] (pretrain.south) -- (calcclab.north);
	\draw[arrow] (traindata.east) -- (pretrain.west);
	\draw[arrow] (traindata.south) |- (calcc.west);

	%\draw[arrow] (node cs:name=traindata,angle=45) |- node[arrowlabel]{all training data, labels removed} (node cs:name=pretrain,angle=160);
	%\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) {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=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=-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);
	\draw[arrow] (calcc.south) -- (maintrainlab.north);
	\draw[arrow] (traindata.south) |- (maintrain.west);
	%\draw[arrow] (node cs:name=traindata,angle=-135) |- node[arrowlabel]{all training data, including labels} (maintrain.west);
	\draw[arrow] (maintrainhyp.west) -- (maintrain.east);


	\begin{pgfonlayer}{foreground}
		\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}
		\node[procbox, fit=(inferenceproc) (inferenceout), label={[label distance = 1, name=inferencelab]above:{\textbf{Inference}}}] (inference) {};
	\end{pgfonlayer}

	\begin{pgfonlayer}{foreground}
		\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) {};
	\end{pgfonlayer}

	\draw[arrow] (maintrain.south) -- (inferencelab.north);
	\draw[arrow] (newdata.east) -- (inference.west);

\end{tikzpicture}
\end{document}
























