Added related works
Signed-off-by: Jim Martens <github@2martens.de>
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@ -130,6 +130,84 @@ with MS COCO classes.
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\chapter{Background and Contribution}
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This chapter will begin with an overview over previous works
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in the field of this thesis. Afterwards the theoretical foundations
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of the work of Miller et al.~\cite{Miller2018} and auto-encoders will
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be explained. The chapter concludes with more details about the
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research question and the intended contribution of this thesis.
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\section{Related Works}
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Novelty detection for object detection is intricately linked with
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open set conditions: the test data can contain unknown classes.
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Bishop~\cite{Bishop1994} investigates the correlation between
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the degree of novel input data and the reliability of network
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outputs. Pimentel et al.~\cite{Pimentel2014} provide a review
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of novelty detection methods published over the previous decade.
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There are two primary pathways that deal with novelty: novelty
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detection using auto-encoders and uncertainty estimation with
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bayesian networks.
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Japkowicz et al.~\cite{Japkowicz1995} introduce a novelty detection
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method based on the hippocampus of Gluck and Meyers~\cite{Gluck1993}
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and use an auto-encoder to recognize novel instances.
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Thompson et al.~\cite{Thompson2002} show that auto-encoders
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can learn "normal" system behaviour implicitly.
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Goodfellow et al.~\cite{Goodfellow2014} introduce adversarial
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networks: a generator that attempts to trick the discriminator
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by generating samples indistinguishable from the real data.
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Makhzani et al.~\cite{Makhzani2015} build on the work of Goodfellow
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and propose adversarial auto-encoders. Richter and
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Roy~\cite{Richter2017} use an auto-encoder to detect novelty.
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Wang et al.~\cite{Wang2018} base upon Goodfellow's work and
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use a generative adversarial network for novelty detection.
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Sabokrou et al.~\cite{Sabokrou2018} implement an end-to-end
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architecture for one-class classification: it consists of two
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deep networks, with one being the novelty detector and the other
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enhancing inliers and distorting outliers.
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Pidhorskyi et al.~\cite{Pidhorskyi2018} take a probabilistic approach
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and compute how likely it is that a sample is generated by the
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inlier distribution.
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Kendall and Gal~\cite{Kendall2017} provide a Bayesian deep learning
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framework that combines input-dependent
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aleatoric\footnote{captures noise inherent in observations}
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uncertainty with epistemic\footnote{uncertainty in the model}
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uncertainty. Lakshminarayanan et al.~\cite{Lakshminarayanan2017}
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implement a predictive uncertainty estimation using deep ensembles
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rather than Bayesian networks. Geifman et al.~\cite{Geifman2018}
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introduce an uncertainty estimation algorithm for non-Bayesian deep
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neural classification that estimates the uncertainty of highly
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confident points using earlier snapshots of the trained model.
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Miller et al.~\cite{Miller2018a} compare merging strategies
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for sampling-based uncertainty techniques in object detection.
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Sensoy et al.~\cite{Sensoy2018} treat prediction confidence
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as subjective opinions: they place a Dirichlet distribution on it.
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The trained predictor for a multi-class classification is also a
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Dirichlet distribution.
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Gal and Ghahramani~\cite{Gal2016} show how dropout can be used
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as a Bayesian approximation. Miller et al.~\cite{Miller2018}
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build upon the work of Miller et al.~\cite{Miller2018a} and
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Gal and Ghahramani: they use dropout sampling under open-set
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conditions for object detection. Mukhoti and Gal~\cite{Mukhoti2018}
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contribute metrics to measure uncertainty for semantic
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segmentation. Wu et al.~\cite{Wu2019} introduce two innovations
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that turn variational Bayes into a robust tool for Bayesian
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networks: they introduce a novel deterministic method to approximate
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moments in neural networks which eliminates gradient variance, and
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they introduce a hierarchical prior for parameters and an
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Empirical Bayes procedure to select prior variances.
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% SSD: \cite{Liu2016}
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% ImageNet: \cite{Deng2009}
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% COCO: \cite{Lin2014}
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% YCB: \cite{Xiang2017}
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% SceneNet: \cite{McCormac2017}
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\chapter{Methods}
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\section{Design of Source Code}
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