From 2280e54fc93e1a38ba5fcc5e18f6f734bdfd681d Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Tue, 27 Aug 2019 16:16:11 +0200 Subject: [PATCH] Removed dummy discussion Signed-off-by: Jim Martens --- body.tex | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/body.tex b/body.tex index 2722dd6..70a907c 100644 --- a/body.tex +++ b/body.tex @@ -659,30 +659,6 @@ from 0.1 to 2.5 as specified in Miller et al.~\cite{Miller2018}. \label{chap:discussion} -To recap, the hypothesis is repeated here. - -\begin{description} - \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. -\end{description} - -Based on the reported results, no clear answer can be given to the -research question; rather new questions emerge: "Can auto-encoders -work on realistic data sets like COCO with multiple different classes -in one image?" In other words: "Is my experience due to -implementation issues or a general theoretical problem of -auto-encoders?" - -Despite best efforts, the results of Miller et al.~\cite{Miller2018} -could not be replicated. This does not show anything though. -To disprove Miller's work, any and all possible ways to replicate -their work must fail. Contrarily, one successful replication -proves the ability to replicate. On the surface, both Miller et al. -and I used the same weights, the same network, and the same -data sets. Only difference of note: they used a Caffe implementation -of SSD, for this thesis the Tensorflow implementation with eager mode -was used. - - \chapter{Closing} \label{chap:closing}