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Finished exposé

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
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@ -41,7 +41,7 @@ of technology to allow adoption in mass. Obviously this setting
poses the question, how such an endeavour can be achieved.
For neural networks there are fundamentally two type of tasks:
regression, and classification. Regression deals with any case
regression and classification. Regression deals with any case
where the goal for the network is to come close to an ideal
function that connects all data points. Classification, however,
describes tasks where the network is supposed to identify the
@ -71,7 +71,7 @@ This goes back to the need for automatic explanation. Such a system
should by itself recognize that the given object is unknown and
hence mark any classification result of the network as meaningless.
Technically there are two slightly different things that deal
with this type of task: model uncertainty, and novelty detection.
with this type of task: model uncertainty and novelty detection.
Model uncertainty can be measured with dropout sampling.
Dropout is usually used only during training but
@ -190,4 +190,214 @@ but not critical for successful completion of the thesis.
\section{Timetable}
% TODO
This timetable is structured by milestones that I want to
achieve. Every milestone has the related tasks grouped beneath it.
The scheduling is done with respect to my full personal calendar
and will only account Monday through Friday at most. Weekends will
not be scheduled work time for the thesis. This allows for
some additional unreliable emergency buffer in the end if things do
not proceed as planned. Furthermore I will only be able to
regularly plan the time between 11 am and 5 pm for working on the
thesis as the evenings are mostly full and regardless of that
fact I do want to reserve free time.
\subsection*{Environment set up}
\textbf{Due date:} 20th March
\begin{description}
\item[Download SceneNet RGB-D to cvpc\{7,8\} computer]
Requires external resource.
\end{description}
\subsection*{Fine-tuned SSD on SceneNet RGB-D}
\textbf{Due date:} 5th April
\begin{description}
\item[Download pre-trained weights of SSD for MS COCO]
This is trivial. Takes not more than two hours.
\item[Modify SSD Keras implementation to work inside masterthesis package]
Should be possible to achieve within one day.
\item[Implement integration of SSD into masterthesis package]
Implementing the glue code between the git submodule and
my own code. Should be doable within one day.
\item[Group SceneNet RGB-D classes to MS COCO classes]
SceneNet contains more classes than COCO. Miller et al have
grouped, for example, various chair classes in SceneNet into
one chair class of COCO. This grouping involves researching
the 80 classes of COCO and finding all related SceneNet
classes and then writing a mapper between them.
All in all this could take up a full day and perhaps slip
into a second one.
\item[Implement variant of SSD with dropout layers (Bayesian SSD)]
This is a rather trivial task as it only involves adding two
Keras dropout layers into SSD. Can be done in one hour.
\item[Fine-tune vanilla SSD on SceneNet RGB-D]
Requires external resource and length of required training is
unknown. Due to two unknown factors (availability of resource,
and length of training) this task can be considered a project
risk.
\item[Fine-tune Bayesian SSD on SceneNet RGB-D]
Similar remarks like the previous task.
\end{description}
The tasks prior to the training could be achievable by the 21st
March if work starts on the 18th. Buffer time will go to the 25th
of March. Training is scheduled to commence as early as possible
but no later than the 26th of March.
Since the SSD network is a proven one, I am confident that this
milestone can be reached and the time between 26th of March and
5th April should provide more than enough time for training.
Once training has started, I can work on tasks from other milestones
so that the training time is used as efficiently as possible.
\subsection*{Fine-tuned GPND on SceneNet RGB-D}
\textbf{Due date:} 12th April
\begin{description}
\item[Adapt GPND implementation for SceneNet RGB-D using MS COCO classes]
Requires research to figure out the exact architecture needed
for a different data set. The code is not well
documented and some logical variables like image size are
sometimes hard-coded, which makes this adaption difficult
and error-prone.
Furthermore, some trial-and-error regarding training successes
is likely needed, which makes this task a project risk.
If the needed architecture was known the time to implement
it would be at most one day. The uncertainty therefore
lies with the research part.
\item[Implement novelty score calculation for GPND]
There is an implementation for this in the original
author's implementation. It would have to be ported
to Tensorflow and integrated into the package structure.
Takes likely one day or two.
\item[Apply insights of GAN stability to GPND implementation]
The insights from the GAN stability\footnote{\url{https://avg.is.tuebingen.mpg.de/publications/meschedericml2018}} research should be applied to
my GPND implementation. Requires research what, if any,
insights can be used for this thesis. The research is
doable within one day and the application of it
within another.
\item[Train GPND on SceneNet RGB-D]
Requires external resource.
In contrast to the SSD network, there are no pre-trained
weights available for the GPND. Therefore it has to be
trained from scratch. Furthermore, it will have to be
trained for every class separately, which prolongs the
training even further. This task than be classified as
project risk.
\end{description}
I will only be able to start working on these tasks on April 1st.
Assuming that the research in the first task goes well, I will
be able to finish the preparatory work on April 5th. Training
could start as early as April 5th. The seven days to the due date
April 12th are tight and maybe it takes longer but this is
the aggressive date I will work towards.
\subsection*{Networks evaluated}
\textbf{Due date:} 10th May
\begin{description}
\item[Implement evaluation pipeline for vanilla SSD]
Involves the implementation of the evaluation steps
according to the chosen metrics. Takes likely two days.
\item[Implement evaluation pipeline for Bayesian SSD]
Involves the implementation of the evaluation steps
for the Bayesian variant. As more is has to be done,
it will likely take three days.
\item[Implement evaluation pipeline for vanilla SSD with GPND for novelty score]
The implementation of this pipeline will take probably two
days.
\item[Run vanilla SSD on test data]
The trained network is run on the test data and the results
are stored. Requires external resource but should be
far quicker than the training and will probably be done
in two days at most.
\item[Run Bayesian SSD on test data]
Similar remarks to previous task.
\item[Run vanilla SSD detections through GPND]
For my approach the SSD detections need to be run through
the GPND to have all the relevant data for evaluation.
Requires external resource. Will take likely two days.
\item[Calculate evaluation metrics for vanilla SSD]
Takes one day.
\item[Calculate evaluation metrics for Bayesian SSD]
Takes one day.
\item[Calculate evaluation metrics for vanilla SSD with GPND]
Takes one day
\end{description}
If I can start on April 15th with the preparatory work it should be
done by April 23rd. The testing runs can begin as early as April 24th
and should finish around April 30th. This leaves the week from
May 6th up to the due date to finish the calculations, which can
happen on the CPU as all the data is already there by then.
\subsection*{Visualizations created}
\textbf{Due date:} 31st May
I won't be able to work on the thesis between May 13th and May 26th
due to the election campaign. I am involved into the campaign already
as of this writing but I hope that up until May 10th both thesis
and campaign can somewhat co-exist.
The visualizations should be creatable within one week from May 27th
to May 31st.
\subsection*{Stretch goals}
\textbf{Due date:} 27th June
As I mentioned earlier, there are no specific tasks for the
stretch goals. If the critical path is finished by the end
of May as planned then the month of June is available for
stretch goals. If the critical path is not finished then
June serves as a buffer zone to prevent spillover into the
writing period.
\subsection*{Thesis writing}
\textbf{Due date:} 30th August
A first complete draft of the thesis should be finished
at the latest by August 16th. The following week I am not
able to work on the thesis but it can be used for feedback.
The last week of August should allow for polishing of the
thesis with a submission-ready candidate by August 30th.
\subsection*{Finishing touches}
\textbf{Due date:} 13th September
The submission requires three printed copies of the thesis,
together with any digital components on a CD glued to the back
of the thesis. A non-editable CD ensures that the code submitted
cannot be modified and will be exactly as submitted when reviewed.
I will use these two weeks to print the copies and to make
last publication steps for the code like improving the code
documentation and adding usage examples.
\subsection*{Colloquium}
Last but not least is the colloquium which will probably take
place within the second half of September. I will prepare
a presentation for the colloquium in the time before such date.
\section{Project Risks}
In this section other project risks will be listed in
addition to those indicated in the timetable section.
The workload for the election campaign, in which I have an
organizational responsibility in addition to being a candidate
myself, could come into conflict with the progress of the
thesis.