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thesis/Main.tex

@@ -196,7 +196,8 @@
     \newsection{Anomaly Detection}{sec:anomaly_detection}
 
     \todo[inline, color=green!40]{cite exists since X and has been used to find anomalous data in many domains and works with all kinds of data types/structures (visual, audio, numbers). examples healthcare (computer vision diagnostics, early detection), financial anomalies (credit card fraud, maybe other example), security/safety video cameras (public, traffic, factories).}
-    \todo[inline, color=green!40]{the goal of these algorithms is to differentiate between normal and anomalous data by finding statistically relevant information which separates the two, since these methods learn how normal data typically is distributed they do not have to have prior knowledge of the types of all anomalies, therefore can potentially detect unseen, unclassified anomalies as well. main challenges when implementing are that its difficult to cleanly separate normal from anormal data and that typically no or very little labeled data is available}
+    \todo[inline, color=green!40]{the goal of these algorithms is to differentiate between normal and anomalous data by finding statistically relevant information which separates the two, since these methods learn how normal data typically is distributed they do not have to have prior knowledge of the types of all anomalies, therefore can potentially detect unseen, unclassified anomalies as well. main challenges when implementing are that its difficult to cleanly separate normal from anormal data}
+    \todo[inline, color=green!40]{typically no or very little labeled data is available and oftentimes the kinds of possible anomalies are unknown and therefore its not possible to label all of them. due to these circumstances anomaly detection methods oftentimes do not rely on labeled data but on the fact that normal circumstances make up the majority of training data (quasi per defintion)}
     \todo[inline, color=green!40]{figure example shows 2d data but anomaly detection methods work with any kind of dimensionality/shape. shows two clusters of normal data with clear boundaries and outside examples of outliers (anomalous data two single points and one cluster), anomaly detection methods learn to draw these boundaries from the training data given to them which can then be used to judge if unseen data is normal or anormal}
     \todo[inline, color=green!40]{as discussed in motivation, and same as in reference paper (rain autonomous driving) we model our problem as an anomaly detection problem where we define that good quality sensor data is normal data and degraded sensor data (in our case due to dust/smoke) is defined as an anomaly. this allows us to quantify the degradation of data by using the anomaly detection method to check how likely new data is an anomaly}
 \iffalse
@@ -220,6 +221,13 @@
     \todo[inline, color=green!40]{find easy illustrative example with figure of semi-supervised learning and include + explain here}
     \todo[inline, color=green!40]{our chosen method DeepSAD is a semi-supervised deep learning method whose workings will be discussed in more detail in secion X}
 
+    \newsection{Autoencoder}{sec:autoencoder}
+    \todo[inline]{autoencoder explanation}
+    \todo[inline, color=green!40]{autoencoders are a neural network architecture archetype (words) whose training target is to reproduce the input data itself - hence the name. the architecture is most commonly a mirrored one consisting of an encoder which transforms input data into a hyperspace represantation in a latent space and a decoder which transforms the latent space into the same data format as the input data (phrasing), this method typically results in the encoder learning to extract the most robust and critical information of the data and the (todo maybe something about the decoder + citation for both). it is used in many domains translations, LLMs, something with images (search example + citations)}
+    \todo[inline, color=green!40]{typical encoder decoder mirrored figure}
+    \todo[inline, color=green!40]{explain figure}
+    \todo[inline, color=green!40]{our chosen method DeepSAD uses an autoencoder to translate input data into a latent space, in which it can more easily differentiate between normal and anomalous data}
+
     \newsection{Lidar - Light Detection and Ranging}{sec:lidar_related_work}
     \todo[inline]{related work in lidar}
     \todo[inline, color=green!40]{the older more commonly known radar works by sending out an electromagnetic wave in the radiofrequency and detecting the time it takes to return (if it returns at all) signalling a reflective object in the path of the radiowave. lidar works on the same principle but sends out a lightray produced by a laser (citation needed) and measuring the time it takes for the ray to return to the sensor. since the speed of light is constant in air the system can calculate the distance between the sensor and the measured point. modern lidar systems send out multiple, often millions of measurement rays per second which results in a three dimensional pointcloud, constructed from the information in which direction the ray was cast and the distance that was measured}
@@ -229,8 +237,17 @@
 
 
 \newchapter{DeepSAD: Semi-Supervised Anomaly Detection}{chap:deepsad}
+    \todo[inline, color=green!40]{DeepSAD is a semi-supervised anomaly detection method proposed in cite, which is based on an unsupervised method (DeepSVDD) and additionally allows for providing some labeled data which is used during the training phase to improve the method's performance}
     \newsection{Algorithm Description}{sec:algorithm_description}
     \todo[inline]{explain deepsad in detail}
+    \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}
+    \todo[inline, color=green!40]{explanation pre-training step: architecture of the autoencoder is dependent on the input data shape, but any data shape is generally permissible. for the autoencoder we do not need any labels since the optimization target is always the input itself. the latent space dimensionality can be chosen based on the input datas complexity (search citations). generally a higher dimensional latent space has more learning capacity but tends to overfit more easily (find cite). the pre-training step is used to find weights for the encoder which genereally extract robust and critical data from the input because TODO read deepsad paper (cite deepsad). as training data typically all data (normal and anomalous) is used during this step.}
+    \todo[inline, color=green!40]{explanation hypersphere center step: an additional positive ramification of the pretraining is that the mean of all pre-training's latent spaces can be used as the hypersphere target around which normal data is supposed to cluster. this is advantageous because it allows the main training to converge faster than choosing a random point in the latent space as hypersphere center. from this point onward the center C is fixed for the main training and inference and does not change anymore.}
+    \todo[inline, color=green!40]{explanation training step: during the main training step the method starts with the pre-trained weights of the encoder but removes the decoder from the architecture since it optimizes the output in the latent space and does not need to reproduce the input data format. it does so by minimizing the geometric distance of each input data's latent space represenation to the previously defined hypersphere center c. Due to normal data being more common in the inputs this results in normal data clustering closely to C and anormal data being pushed away from it. additionally during this step the labeled data is used to more correctly map normal and anormal data}
+    \todo[inline, color=green!40]{explanation inference step: with the trained network we can transform new input data into the latent space and calculate its distance from the hypersphere center which will be smaller the more confident the network is in the data being normal and larger the more likely the data is anomalous. This output score is an analog value dependent on multiple factors like the latent space dimensionality, encoder architecture and ??? and has to be interpreted further to be used (for example thresholding)}
+
+    \newsection{Algorithm Details and Hyperparameters}{sec:algorithm_details}
+    \todo[inline]{backpropagation optimization formula, hyperaparameters explanation}
 
     \newsection{Advantages and Limitations}{sec:advantages_limitations}
     \todo[inline]{semi supervised, learns normality by amount of data (no labeling/ground truth required), very few labels for better training to specific situation}