71 lines
2.0 KiB
TeX
71 lines
2.0 KiB
TeX
% acronyms
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\newacronym{MC}{MC}{Monte Carlo}
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\newacronym{MCDO}{MCDO}{Monte Carlo Dropout}
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\newacronym{MCBN}{MCBN}{Monte Carlo Batch Normalisation}
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\newacronym{MLP}{MLP}{multilayer perceptron}
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\newacronym{NMS}{NMS}{non-maximum suppression}
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\newacronym{OSE}{OSE}{open set error}
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\newacronym{SSD}{SSD}{Single Shot MultiBox Detector}
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\newacronym{pdf}{pdf}{probabilistic density function}
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% terms
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\newglossaryentry{BGR}{
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name={BGR},
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description={
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stands for the three colour channels blue, green, and red in this order
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}
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}
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\newglossaryentry{Caffe}{
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name={Caffe},
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description={
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is a deep learning framework written in C++
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}
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}
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\newglossaryentry{CCTV}{
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name={CCTV},
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description={
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stands for closed-circuit television or video surveillance
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}
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}
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\newglossaryentry{Dirichlet distribution}{
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name={Dirichlet distribution},
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description={
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is named after Peter Dirichlet and a family of probability
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distributions
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}
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}
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\newglossaryentry{entropy}{
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name={entropy},
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description={
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describes the amount of information provided by something. More likely
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events have a lower entropy than rare events. In case of classification probabilities, uniform predictions contain more information than predictions with a clear "winner"
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}
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}
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\newglossaryentry{Hopfield network}{
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name={Hopfield network},
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description={
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is a recurrent neural network. Used as "associative" memory
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systems with binary thresholds. Guaranteed to converge to local
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minimum, this can be the wrong one though
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}
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}
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\newglossaryentry{posterior}{
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name={posterior},
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description={
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probability output of a neural network
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}
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}
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\newglossaryentry{RGB}{
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name={RGB},
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description={
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stands for the three colour channels red, green, and blue in this order
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}
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}
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\newglossaryentry{vanilla}
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{
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name={vanilla},
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description={
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is used to describe the original state of something
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}
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}
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