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Unified headlines to be in title case and changed open-set to open set

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-03-07 16:43:52 +01:00
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@ -50,9 +50,9 @@ describes tasks where the network is supposed to identify the
class of any given input. In this thesis, I will focus on
classification.
\subsection*{Object detection in open-set conditions}
\subsection*{Object Detection in Open Set Conditions}
More specifically, I will look at object detection in the open-set
More specifically, I will look at object detection in the open set
conditions. In non-technical words this effectively describes
the kind of situation you encounter with CCTV cameras or robots
outside of a laboratory. Both use cameras that record
@ -108,7 +108,7 @@ auto-encoder, a novelty score is calculated. A low novelty
score signals a known object. The opposite is true for a high
novelty score.
\subsection*{Research question}
\subsection*{Research Question}
Given these two approaches to solve the explanation task of above,
it comes down to performance. At the end of the day the best
@ -131,14 +131,14 @@ being equal. With respect to auto-encoders a recent implementation
of an adversarial auto-encoder\cite{Pidhorskyi2018} will be used.
\paragraph{Hypothesis} Novelty detection using auto-encoders
delivers similar or better object detection performance under open-set
delivers similar or better object detection performance under open set
conditions while being less computationally expensive compared to
dropout sampling.
\paragraph{Contribution}
The contribution of this thesis is a comparison between dropout
sampling and auto-encoding with respect to the overall performance
of both for object detection in the open-set conditions using
of both for object detection in the open set conditions using
the SSD network for object detection and the SceneNet RGB-D data set
with MS COCO classes.
@ -175,7 +175,7 @@ and intractable problem of averaging over all weights in the network
is replaced with an optimisation task, where the parameters of the
simple distribution are optimised over\cite{Kendall2017}.
\subsubsection*{Dropout variational inference}
\subsubsection*{Dropout Variational Inference}
Kendall and Gal\cite{Kendall2017} showed an approximation for
classfication and recognition tasks. Dropout variational inference
@ -199,7 +199,7 @@ With this dropout sampling technique \(n\) model weights
of the network with respect to the classification is given by
the entropy \(H(\mathbf{q}) = - \sum_i q_i \cdot \log q_i\).
\subsubsection*{Dropout sampling for object detection}
\subsubsection*{Dropout Sampling for Object Detection}
Miller et al\cite{Miller2018} apply the dropout sampling to
object detection. In that case \(\mathbf{W}\) represents the
@ -230,7 +230,7 @@ distribution means that no class is more likely than another, which
is a perfect example of maximum uncertainty. Conversely, if
one class has a very high probability the entropy will be low.
In open-set conditions it can be expected that falsely generated
In open set conditions it can be expected that falsely generated
detections for unknown object classes have a higher label
uncertainty. A treshold on the entropy \(H(\mathbf{q}_i)\) can then
be used to identify and reject these false positive cases.
@ -370,7 +370,7 @@ 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
delivers similar or better object detection performance under open set
conditions while being less computationally expensive compared to
dropout sampling.\\
@ -389,7 +389,7 @@ the thesis:
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
performance and computational performance under open set conditions
using the SceneNet RGB-D data set with the MS COCO classes as
"known" object classes.