From 1a1b6937f62d3777627ecd0158e12b75f6fd4f6f Mon Sep 17 00:00:00 2001
From: Jim Martens <github@2martens.de>
Date: Thu, 1 Aug 2019 16:52:59 +0200
Subject: [PATCH] Added related works

Signed-off-by: Jim Martens <github@2martens.de>
---
 body.tex | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)

diff --git a/body.tex b/body.tex
index 6236746..aaa77ed 100644
--- a/body.tex
+++ b/body.tex
@@ -130,6 +130,84 @@ with MS COCO classes.
 
 \chapter{Background and Contribution}
 
+This chapter will begin with an overview over previous works
+in the field of this thesis. Afterwards the theoretical foundations
+of the work of Miller et al.~\cite{Miller2018} and auto-encoders will
+be explained. The chapter concludes with more details about the
+research question and the intended contribution of this thesis.
+
+\section{Related Works}
+
+Novelty detection for object detection is intricately linked with
+open set conditions: the test data can contain unknown classes.
+Bishop~\cite{Bishop1994} investigates the correlation between
+the degree of novel input data and the reliability of network
+outputs. Pimentel et al.~\cite{Pimentel2014} provide a review
+of novelty detection methods published over the previous decade.
+
+There are two primary pathways that deal with novelty: novelty
+detection using auto-encoders and uncertainty estimation with
+bayesian networks.
+
+Japkowicz et al.~\cite{Japkowicz1995} introduce a novelty detection
+method based on the hippocampus of Gluck and Meyers~\cite{Gluck1993}
+and use an auto-encoder to recognize novel instances.
+Thompson et al.~\cite{Thompson2002} show that auto-encoders
+can learn "normal" system behaviour implicitly.
+Goodfellow et al.~\cite{Goodfellow2014} introduce adversarial
+networks: a generator that attempts to trick the discriminator
+by generating samples indistinguishable from the real data.
+Makhzani et al.~\cite{Makhzani2015} build on the work of Goodfellow
+and propose adversarial auto-encoders. Richter and
+Roy~\cite{Richter2017} use an auto-encoder to detect novelty.
+
+Wang et al.~\cite{Wang2018} base upon Goodfellow's work and
+use a generative adversarial network for novelty detection.
+Sabokrou et al.~\cite{Sabokrou2018} implement an end-to-end
+architecture for one-class classification: it consists of two
+deep networks, with one being the novelty detector and the other
+enhancing inliers and distorting outliers.
+Pidhorskyi et al.~\cite{Pidhorskyi2018} take a probabilistic approach
+and compute how likely it is that a sample is generated by the
+inlier distribution.
+
+Kendall and Gal~\cite{Kendall2017} provide a Bayesian deep learning
+framework that combines input-dependent
+aleatoric\footnote{captures noise inherent in observations}
+uncertainty with epistemic\footnote{uncertainty in the model}
+uncertainty. Lakshminarayanan et al.~\cite{Lakshminarayanan2017}
+implement a predictive uncertainty estimation using deep ensembles
+rather than Bayesian networks. Geifman et al.~\cite{Geifman2018}
+introduce an uncertainty estimation algorithm for non-Bayesian deep
+neural classification that estimates the uncertainty of highly
+confident points using earlier snapshots of the trained model.
+Miller et al.~\cite{Miller2018a} compare merging strategies
+for sampling-based uncertainty techniques in object detection.
+Sensoy et al.~\cite{Sensoy2018} treat prediction confidence
+as subjective opinions: they place a Dirichlet distribution on it.
+The trained predictor for a multi-class classification is also a
+Dirichlet distribution.
+
+Gal and Ghahramani~\cite{Gal2016} show how dropout can be used
+as a Bayesian approximation. Miller et al.~\cite{Miller2018}
+build upon the work of Miller et al.~\cite{Miller2018a} and
+Gal and Ghahramani: they use dropout sampling under open-set
+conditions for object detection. Mukhoti and Gal~\cite{Mukhoti2018}
+contribute metrics to measure uncertainty for semantic
+segmentation. Wu et al.~\cite{Wu2019} introduce two innovations
+that turn variational Bayes into a robust tool for Bayesian
+networks: they introduce a novel deterministic method to approximate
+moments in neural networks which eliminates gradient variance, and
+they introduce a hierarchical prior for parameters and an
+Empirical Bayes procedure to select prior variances.
+
+
+% SSD: \cite{Liu2016}
+% ImageNet: \cite{Deng2009}
+% COCO: \cite{Lin2014}
+% YCB: \cite{Xiang2017}
+% SceneNet: \cite{McCormac2017}
+
 \chapter{Methods}
 
 \section{Design of Source Code}