reworked deepsad procedure diagram

thesis/Main.tex

@@ -359,7 +359,6 @@ In the main training step, DeepSAD's network is trained using SGD backpropagatio
 
 \fig{deepsad_procedure}{diagrams/deepsad_procedure/deepsad_procedure}{(WORK IN PROGRESS) Depiction of DeepSAD's training procedure, including data flows and tweakable hyperparameters.}
 
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 To infer if a previously unknown data sample is normal or anomalous, the sample is fed in a forward-pass through the fully trained network. During inference, the centroid $\mathbf{c}$ needs to be known, to calculate the geometric distance of the samples latent representation to $\mathbf{c}$. This distance is tantamount to an anomaly score, which correlates with the likelihood of the sample being anomalous. Due to differences in input data type, training success and latent space dimensionality, the anomaly score's magnitude has to be judged on an individual basis for each trained network. This means, scores produced by one network that signify normal data, may very well clearly indicate an anomaly for another network. The geometric distance between two points in space is a scalar analog value, therefore post-processing of the score is necessary to achieve a binary classification of normal and anomalous if desired.
 
 DeepSAD's full training and inference procedure is visualized in figure~\ref{fig:deepsad_procedure}, which gives a comprehensive overview of the dataflows, tuneable hyperparameters and individual steps involved.