From 3769760ab5251707946c822bdde8b2011cbb101d Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Thu, 7 Mar 2019 16:41:41 +0100 Subject: [PATCH] Written the scientific contribution Signed-off-by: Jim Martens --- body_expose.tex | 62 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/body_expose.tex b/body_expose.tex index 94cedee..be03d7f 100644 --- a/body_expose.tex +++ b/body_expose.tex @@ -333,6 +333,68 @@ it on the MNIST\cite{Lecun1998} data set, once for every digit. \section{Contribution} +This section will outline what exactly the scientific as well as +technical contribution of this thesis will be. + +\subsection*{Scientific Contribution} + +Miller et al\cite{Miller2018} use the SSD\cite{Liu2016} network +extended with dropout layers and run multiple forward passes +during the testing phase for every image. Considering the number +of images in the SceneNet RGB-D\cite{McCormac2017} data set, these +forward passes will take considerable time. It could be faster +to only run one forward pass and then use the auto-encoder for +novelty detection. However, the auto-encoder can only work +with one detection at the time and must be called for every +detection of the object detector separately. Therefore, +it is interesting to investigate whether the second approach +is indeed faster than the first. + +Dropout sampling uses the entropy to identify false positive +cases. Such identified detections are discarded, which allows for +a better object detection performance. The GPND approach uses +the auto-encoder losses and results to identify novel cases and +therefore mark detections as false positive. Subsequently these +detections can be discarded as well. By comparing the object +detection performance after discarding the identified false +positive cases, the effectiveness of both approaches can be +compared with each other. It is interesting to research if the +GPND approach results in a better object detection performance +than the dropout sampling provides. + +The formulated hypothesis, which is repeated after this paragraph, +combines both aspects and requires a similar or better result in +both of them. As a consequence it will be falsified if +the computational performance of the GPND approach is not better than +the one of dropout sampling or if the object detection performance +is worse. + +\paragraph{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.\\ + +There are three possible scenarios that can be the result of +the thesis: +\begin{itemize} + \item the hypothesis is confirmed: Win-Win situation where + switching to GPND is straightforward. + \item one of the conditions fails: Win-Lose situation where + it is a trade-off between object detection performance and + computational performance. One approach will be better in + one thing and the other approach in the other thing. + \item both conditions fail: Lose-Lose situation where + dropout sampling is the best in both aspects. +\end{itemize} + +Summarising, the scientific contribution is a comparison between +dropout sampling and GPND with respect to both object detection +performance and computational performance under open-set conditions +using the SceneNet RGB-D data set with the MS COCO classes as +"known" object classes. + +\subsection*{Technical Contribution} + \chapter{Thesis as a project} After introducing the topic and the general task ahead, this part of