From e45d6691369ff04183f65e72822fd4d1077bda21 Mon Sep 17 00:00:00 2001 From: Jan Kowalczyk Date: Wed, 20 Aug 2025 18:17:39 +0200 Subject: [PATCH] deepsad todos wip --- thesis/Main.tex | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/thesis/Main.tex b/thesis/Main.tex index cbd419e..c1b3796 100755 --- a/thesis/Main.tex +++ b/thesis/Main.tex @@ -575,8 +575,13 @@ The first term of \ref{eq:deepsad_optimization_objective} stays mostly the same, The neural network architecture of DeepSAD is not fixed but rather dependent on the data type the algorithm is supposed to operate on. This is due to the way it employs an autoencoder for pre-training and the encoder part of the network for its main training step. This makes the adaption of an autoencoder architecture suitable to the specific application necessary but also allows for flexibility in choosing a fitting architecture depending on the application's requirements. For this reason the specific architecture employed, may be considered a hyperparameter of the Deep SAD algorithm. -\todo[inline]{Talk about choosing the correct architecture (give example receptive fields for image data from object detection?)} +%The latent space dimensionality is also freely choosable and should intuitively be chosen as small as possible while still being able to capture + +% not necessary since arch is discussed in setup chhapter \todo[inline]{Talk about choosing the correct architecture (give example receptive fields for image data from object detection?)} + \todo[inline]{latent space size, talk about auto encoder performance, trying out sensible dimensionalities and find reconstruction elbow, choose smallest possible, but as large as necessary} +\todo[inline]{latent space size for AE shows that most likely all of the important data may be captured inside this dim (since recons;truction is possible) but we may only require some of the encoded patterns to differentiate normal from anomaly so smaller may still be possible? should this be discussed here or not? maybe only discuss; AE considerations and then move this discussion to discussion / results} + \todo[inline]{eta, think of possible important scenarios, learning rate, epochs} %\todo[inline, color=green!40]{Core idea of the algorithm is to learn a transformation to map input data into a latent space where normal data clusters close together and anomalous data gets mapped further away. to achieve this the methods first includes a pretraining step of an auto-encoder to extract the most relevant information, second it fixes a hypersphere center in the auto-encoders latent space as a target point for normal data and third it traings the network to map normal data closer to that hypersphere center. Fourth The resulting network can map new data into this latent space and interpret its distance from the hypersphere center as an anomaly score which is larger the more anomalous the datapoint is} @@ -588,10 +593,6 @@ The neural network architecture of DeepSAD is not fixed but rather dependent on %\todo[inline, color=green!40]{in formula X we see the optimization target of the algorithm. explain in one paragraph the variables in the optimization formula} %\todo[inline, color=green!40]{explain the three terms (unlabeled, labeled, regularization)} -\newsection{advantages_limitations}{Advantages and Limitations} -\todo[inline]{unsure if this section makes sense, what content would be here?} -%\todo[inline]{semi supervised, learns normality by amount of data (no labeling/ground truth required), very few labels for better training to specific situation} - \newchapter{data_preprocessing}{Data and Preprocessing} \threadtodo