Updated introduction and made it more concise

Signed-off-by: Jim Martens <github@2martens.de>

body.tex

@@ -8,34 +8,21 @@ Famous examples like the automatic soap dispenser which does not
 recognize the hand of a black person but dispenses soap when presented
 with a paper towel raise the question of bias in computer
 systems~\cite{Friedman1996}. Related to this ethical question regarding
-the design of so called algorithms, a term often used in public
-discourse for applied neural networks, is the question of
+the design of so called algorithms is the question of
 algorithmic accountability~\cite{Diakopoulos2014}.
 
-The charm of supervised neural networks, that they can learn from
-input-output relations and figure out by themselves what connections
-are necessary for that, is also their Achilles heel. This feature
-makes them effectively black boxes. It is possible to question the
-training environment, like potential biases inside the data sets, or
-the engineers constructing the networks but it is not really possible
-to question the internal calculations made by a network. On the one
-hand, one might argue, it is only math and nothing magical that
-happens inside these networks. Clearly it is possible, albeit a chore,
-to manually follow the calculations of any given trained network.
-After all it is executed on a computer and at the lowest level only
-uses basic math that does not differ between humans and computers. On
-the other hand not everyone is capable of doing so and more
-importantly it does not reveal any answers to questions of causality.
+Supervised neural networks learn from input-output relations and
+figure out by themselves what connections are necessary for that.
+This feature is also their Achilles heel: it makes them effectively
+black boxes and prevents any answers to questions of causality.
 
 However, these questions of causility are of enormous consequence when
-neural networks are used, for example, in predictive policing. Is a
-correlation, a coincidence, enough to bring forth negative consequences
+results of neural networks are used to make life changing decisions:
+Is a correlation enough to bring forth negative consequences
 for a particular person? And if so, what is the possible defence
 against math? Similar questions can be raised when looking at computer
 vision networks that might be used together with so called smart
-CCTV cameras, for example, like those tested at the train station
-Berlin Südkreuz. What if a network implies you committed suspicious
-behaviour?
+CCTV cameras to discover suspicious activity.
 
 This leads to the need for neural networks to explain their results.
 Such an explanation must come from the network or an attached piece
@@ -103,17 +90,17 @@ representation of the input and has to find a decompression
 that reconstructs the input as accurate as possible. During
 training these auto-encoders learn to reproduce a certain group
 of object classes. The actual novelty detection takes place
-during testing. Given an image, and the output and loss of the
+during testing: Given an image, and the output and loss of the
 auto-encoder, a novelty score is calculated. A low novelty
 score signals a known object. The opposite is true for a high
 novelty score.
 
 \subsection*{Research Question}
 
-Given these two approaches to solve the explanation task of above,
-it comes down to performance. At the end of the day the best
-theoretical idea does not help in solving the task if it cannot
-be implemented in a performant way. Miller et al. have shown
+Both presented approaches describe one way to solve the aforementioned
+problem of explanation. They can be differentiated by measuring
+their performance: the best theoretical idea is useless if it does
+not perform well. Miller et al. have shown
 some success in using dropout sampling. However, the many forward
 passes during testing for every image seem computationally expensive.
 In comparison a single run through a trained auto-encoder seems
@@ -124,11 +111,10 @@ use the work of Miller et al. as baseline to compare against.
 They use the SSD~\cite{Liu2016} network for object detection,
 modified by added dropout layers, and the SceneNet
 RGB-D~\cite{McCormac2017} data set using the MS COCO~\cite{Lin2014}
-classes. Instead of dropout sampling my approach will use
-an auto-encoder for novelty detection with all else, like
-using SSD for object detection and the SceneNet RGB-D data set,
-being equal. With respect to auto-encoders a recent implementation
-of an adversarial auto-encoder~\cite{Pidhorskyi2018} will be used.
+classes. I will use a simple implementation of an auto-encoder and
+novelty detection to compare with the work of Miller et al.
+SSD for the object detection and SceneNet RGB-D as the data
+set are used for both approaches.
 
 \paragraph{Hypothesis} Novelty detection using auto-encoders
 delivers similar or better object detection performance under open set