From b52391ded365e178382a6dac295fbf7037f35bda Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Thu, 7 Mar 2019 17:37:08 +0100 Subject: [PATCH] Written technical contribution Signed-off-by: Jim Martens --- body_expose.tex | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/body_expose.tex b/body_expose.tex index 888364d..39cc9b1 100644 --- a/body_expose.tex +++ b/body_expose.tex @@ -409,6 +409,45 @@ and do not have a winning class label of "unknown". \subsection*{Technical Contribution} +Technical contribution includes all contributions +that are not necessarily new in the scientific sense but are a +meaningful engineering contribution in itself. + +There is no available source code for the work of +Miller et al\cite{Miller2018}, which necessitates a re-implementation +of their work by myself. The contribution is the fine-tuning of +an SSD model pre-trained on ImageNet\cite{Deng2009}, extended by +dropout layers, to the SceneNet RGB-D data set using MS COCO classes +as the known classes for SSD. +As MS COCO classes are more general than SceneNet RGB-D classes this +also requires a mapping from one set of classes to the other. +This entire contribution is technical and only re-implements +what Miller et al have already done. It is expected that the +evaluation of the results using this self-trained model will +reproduce the results of Miller et al. + +For GPND source code is available but only for MNIST and using +PyTorch. Therefore, the source code has to be transcoded from +PyTorch to Tensorflow. Furthermore, it must be made compatible +with the SceneNet RGB-D as the architecture is tailored to MNIST. +The mapping from SceneNet RGB-D to MS COCO applies here as well and +can therefore be considered a separate contribution. A fine-tuned +SSD is required also but this time without added dropout layers. +Additionally, it is necessary to train the auto-encoder for every +known class separately. + +To summarise it in a list, the following separate deliverables +are contributed: + +\begin{itemize} + \item source code for dropout sampling compatible with Tensorflow + \item source code for GPND compatible with Tensorflow + \item mapping from SceneNet RGB-D classes to MS COCO classes + \item vanilla SSD model fine-tuned on SceneNet RGB-D + \item dropout SSD model fine-tuned on SceneNet RGB-D + \item auto-encoder model trained separately on every MS COCO class +\end{itemize} + \chapter{Thesis as a project} After introducing the topic and the general task ahead, this part of