Added skeleton discussion
Signed-off-by: Jim Martens <github@2martens.de>
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body.tex
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@ -735,4 +735,29 @@ detection is out of the question under theses circumstances.
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\chapter{Discussion}
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To recap, the hypothesis is repeated here.
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\begin{description}
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\item[Hypothesis] Novelty detection using auto-encoders delivers similar or better object detection performance under open set conditions while being less computationally expensive compared to dropout sampling.
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\end{description}
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Based on the reported results, no clear answer can be given to the
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research question; rather new questions emerge: "Can auto-encoders
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work on realistic data sets like COCO with multiple different classes
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in one image?" In other words: "Is my experience due to
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implementation issues or a general theoretical problem of
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auto-encoders?"
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Despite best efforts, the results of Miller et al.~\cite{Miller2018}
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could not be replicated. This does not show anything though.
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To disprove Miller's work, any and all possible ways to replicate
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their work must fail. Contrarily, one successful replication
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proves the ability to replicate. On the surface, both Miller et al.
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and I used the same weights, the same network, and the same
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data sets. Only difference of note: they used a Caffe implementation
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of SSD, for this thesis the Tensorflow implementation with eager mode
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was used.
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\chapter{Closing}
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