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\section{Conclusion and Outlook}\label{sec:conclusion-and-outlook}
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\section{Experimental Results}\label{sec:experimental-results}
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\subsubsection{Is Few-Shot learning a suitable fit for anomaly detection?}
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Should Few-Shot learning be used for anomaly detection tasks?
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How does it compare to well established algorithms such as Patchcore or EfficientAD?
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\subsubsection{How does disbalancing the Shot number affect performance?}
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Does giving the Few-Shot learner more good than bad samples improve the model performance?
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||||||
\subsubsection{How does the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?}
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How much does the performance improve if only detecting an anomaly or not?
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How does it compare to PatchCore and EfficientAD?
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\subsubsection{Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?}
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I've tried different distance measures $\rightarrow$ but results are pretty much the same.
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\section{Implementation}\label{sec:implementation}
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\subsection{Experiment Setup}\label{subsec:experiment-setup}
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% todo
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todo setup of experiments, which classes used, nr of samples
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kinds of experiments which lead to graphs
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\subsection{Jupyter}\label{subsec:jupyter}
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To get accurate performance measures the active-learning process was implemented in a Jupyter notebook first.
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A straight forward machine-learning pipeline was implemented with the help of Pytorch and RESNet-18.
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Moreover, the Dataset was manually imported with the help of a custom torch dataloader and preprocessed with random augmentations.
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After each loop iteration the Area Under the Curve (AUC) was calculated over the validation set to get a performance measure.
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\section{Introduction}\label{sec:introduction}
|
|
||||||
\subsection{Motivation}\label{subsec:motivation}
|
|
||||||
Anomaly detection has especially in the industrial and automotive field essential importance.
|
|
||||||
Lots of assembly lines need visual inspection to find errors often with the help of camera systems.
|
|
||||||
Machine learning helped the field to advance a lot in the past.
|
|
||||||
PatchCore and EfficientAD are state of the art algorithms trained only on good data and then detect anomalies within unseen (but similar) data.
|
|
||||||
One of their problems is the need of lots of training data and time to train.
|
|
||||||
Few-Shot learning might be a suitable alternative with essentially lowered train time.
|
|
||||||
|
|
||||||
In this thesis the performance of 3 Few-Shot learning algorithms will be compared in the field of anomaly detection.
|
|
||||||
Moreover, few-shot learning might be able not only to detect anomalies but also to detect the anomaly class.
|
|
||||||
|
|
||||||
\subsection{Research Questions}\label{subsec:research-questions}
|
|
||||||
|
|
||||||
\subsubsection{Is Few-Shot learning a suitable fit for anomaly detection?}
|
|
||||||
|
|
||||||
Should Few-Shot learning be used for anomaly detection tasks?
|
|
||||||
How does it compare to well established algorithms such as Patchcore or EfficientAD?
|
|
||||||
|
|
||||||
\subsubsection{How does disbalancing the Shot number affect performance?}
|
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||||||
Does giving the Few-Shot learner more good than bad samples improve the model performance?
|
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||||||
|
|
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\subsubsection{How does the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?}
|
|
||||||
How much does the performance improve if only detecting an anomaly or not?
|
|
||||||
How does it compare to PatchCore and EfficientAD?
|
|
||||||
|
|
||||||
\subsubsection{Extra: How does Euclidean distance compare to Cosine-similarity when using ResNet as a feature-extractor?}
|
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I've tried different distance measures $\rightarrow$ but results are pretty much the same.
|
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\subsection{Outline}\label{subsec:outline}
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todo
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\normalfont B\kern-0.5em{\scshape i\kern-0.25em b}\kern-0.8em\TeX}}}
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\acmConference{Minimize labeling effort of Binary classification Tasks with Active learning}{2023}{Linz}
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\fi
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% Document
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\begin{document}
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|
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||||||
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\title{Few shot learning for anomaly detection\\ Bachelor Thesis for AI}
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|
||||||
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|
|
||||||
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|
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||||||
\author{Lukas Heiligenbrunner}
|
|
||||||
|
|
||||||
\if\ieee1
|
|
||||||
\email{k12104785@students.jku.at}
|
|
||||||
\affiliation{%
|
|
||||||
\institution{Johannes Kepler University Linz}
|
|
||||||
\city{Linz}
|
|
||||||
\state{Upperaustria}
|
|
||||||
\country{Austria}
|
|
||||||
\postcode{4020}
|
|
||||||
}
|
|
||||||
\else
|
|
||||||
\institute{Johannes Kepler University Linz}
|
|
||||||
\fi
|
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|
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||||||
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
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||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
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|
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||||||
|
|
||||||
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|
|
||||||
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|
|
||||||
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|
|
||||||
\if\ieee0
|
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||||||
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|
||||||
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|
|
||||||
|
|
||||||
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|
|
||||||
Todo abstract!!
|
|
||||||
\end{abstract}
|
|
||||||
|
|
||||||
%%
|
|
||||||
%% Keywords. The author(s) should pick words that accurately describe
|
|
||||||
%% the work being presented. Separate the keywords with commas.
|
|
||||||
\if\ieee1
|
|
||||||
\keywords{neural networks, ResNET, pseudo-labeling, active-learning}
|
|
||||||
\fi
|
|
||||||
|
|
||||||
%\received{20 February 2007}
|
|
||||||
%\received[revised]{12 March 2009}
|
|
||||||
%\received[accepted]{5 June 2009}
|
|
||||||
|
|
||||||
%%
|
|
||||||
%% This command processes the author and affiliation and title
|
|
||||||
%% information and builds the first part of the formatted document.
|
|
||||||
\if\ieee1
|
|
||||||
\maketitle
|
|
||||||
\fi
|
|
||||||
\input{introduction}
|
|
||||||
\input{materialandmethods}
|
|
||||||
\input{implementation}
|
|
||||||
\input{experimentalresults}
|
|
||||||
\input{conclusionandoutlook}
|
|
||||||
|
|
||||||
%% The next two lines define the bibliography style to be used, and
|
|
||||||
%% the bibliography file.
|
|
||||||
\bibliographystyle{ACM-Reference-Format}
|
|
||||||
\bibliography{../src/sources}
|
|
||||||
|
|
||||||
%%
|
|
||||||
%% If your work has an appendix, this is the place to put it.
|
|
||||||
\appendix
|
|
||||||
|
|
||||||
% appendix
|
|
||||||
|
|
||||||
\end{document}
|
|
||||||
\endinput
|
|
||||||
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|
|||||||
\section{Material and Methods}\label{sec:material-and-methods}
|
|
||||||
|
|
||||||
\subsection{Material}\label{subsec:material}
|
|
||||||
|
|
||||||
\subsubsection{MVTec AD}\label{subsubsec:mvtecad}
|
|
||||||
MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection.
|
|
||||||
It contains over 5000 high-resolution images divided into fifteen different object and texture categories.
|
|
||||||
Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
|
|
||||||
|
|
||||||
% todo source for https://www.mvtec.com/company/research/datasets/mvtec-ad
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% todo example image
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%\begin{figure}
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% \centering
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% \includegraphics[width=\linewidth/2]{../rsc/muffin_chiauaua_poster}
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% \caption{Sample images from dataset. \cite{muffinsvschiuahuakaggle_poster}}
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% \label{fig:roc-example}
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%\end{figure}
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\subsection{Methods}\label{subsec:methods}
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\subsubsection{Few-Shot Learning}
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Few-Shot learning is a subfield of machine-learning which aims to train a classification-model with just a few or no samples at all.
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In contrast to traditional supervised learning where a huge amount of labeled data is required is to generalize well to unseen data.
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So the model is prone to overfitting to the few training samples.
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Typically a few-shot leaning task consists of a support and query set.
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Where the support-set contains the training data and the query set the evaluation data for real world evaluation.
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A common way to format a few-shot leaning problem is using n-way k-shot notation.
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For Example 3 target classeas and 5 samples per class for training might be a 3-way 5-shot few-shot classification problem.
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A classical example of how such a model might work is a prototypical network.
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These models learn a representation of each class and classify new examples based on proximity to these representations in an embedding space.
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The first and easiest method of this bachelor thesis uses a simple ResNet to calucalte those embeddings and is basically a simple prototypical netowrk.
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See %todo link to this section
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% todo proper source
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\subsubsection{Generalisation from few samples}
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\subsubsection{Patchcore}
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%todo also show values how they perform on MVTec AD
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\subsubsection{EfficientAD}
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todo stuff~\cite{patchcorepaper}
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% https://arxiv.org/pdf/2106.08265
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todo stuff\cite{efficientADpaper}
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% https://arxiv.org/pdf/2303.14535
|
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\subsubsection{Jupyter Notebook}\label{subsubsec:jupyternb}
|
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|
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A Jupyter notebook is a shareable document which combines code and its output, text and visualizations.
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The notebook along with the editor provides a environment for fast prototyping and data analysis.
|
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It is widely used in the data science, mathematics and machine learning community.
|
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|
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In the context of this practical work it can be used to test and evaluate the active learning loop before implementing it in a Dagster pipeline. \cite{jupyter}
|
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|
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\subsubsection{CNN}
|
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Convolutional neural networks are especially good model architectures for processing images, speech and audio signals.
|
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A CNN typically consists of Convolutional layers, pooling layers and fully connected layers.
|
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||||||
Convolutional layers are a set of learnable kernels (filters).
|
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Each filter performs a convolution operation by sliding a window over every pixel of the image.
|
|
||||||
On each pixel a dot product creates a feature map.
|
|
||||||
Convolutional layers capture features like edges, textures or shapes.
|
|
||||||
Pooling layers sample down the feature maps created by the convolutional layers.
|
|
||||||
This helps reducing the computational complexity of the overall network and help with overfitting.
|
|
||||||
Common pooling layers include average- and max pooling.
|
|
||||||
Finally, after some convolution layers the feature map is flattened and passed to a network of fully connected layers to perform a classification or regression task.
|
|
||||||
Figure~\ref{fig:cnn-architecture} shows a typical binary classification task.
|
|
||||||
\cite{cnnintro}
|
|
||||||
|
|
||||||
\begin{figure}
|
|
||||||
\centering
|
|
||||||
\includegraphics[width=\linewidth]{../rsc/cnn_architecture}
|
|
||||||
\caption{Architecture convolutional neural network. \cite{cnnarchitectureimg}}
|
|
||||||
\label{fig:cnn-architecture}
|
|
||||||
\end{figure}
|
|
||||||
|
|
||||||
\subsubsection{RESNet}
|
|
||||||
|
|
||||||
Residual neural networks are a special type of neural network architecture.
|
|
||||||
They are especially good for deep learning and have been used in many state-of-the-art computer vision tasks.
|
|
||||||
The main idea behind ResNet is the skip connection.
|
|
||||||
The skip connection is a direct connection from one layer to another layer which is not the next layer.
|
|
||||||
This helps to avoid the vanishing gradient problem and helps with the training of very deep networks.
|
|
||||||
ResNet has proven to be very successful in many computer vision tasks and is used in this practical work for the classification task.
|
|
||||||
There are several different ResNet architectures, the most common are ResNet-18, ResNet-34, ResNet-50, ResNet-101 and ResNet-152. \cite{resnet}
|
|
||||||
|
|
||||||
Since the dataset is relatively small and the two class classification task is relatively easy (for such a large model) the ResNet-18 architecture is used in this practical work.
|
|
||||||
|
|
||||||
\subsubsection{CAML}
|
|
||||||
Todo
|
|
||||||
\subsubsection{P$>$M$>$F}
|
|
||||||
Todo
|
|
||||||
|
|
||||||
\subsubsection{Softmax}
|
|
||||||
|
|
||||||
The Softmax function~\eqref{eq:softmax}\cite{liang2017soft} converts $n$ numbers of a vector into a probability distribution.
|
|
||||||
Its a generalization of the Sigmoid function and often used as an Activation Layer in neural networks.
|
|
||||||
\begin{equation}\label{eq:softmax}
|
|
||||||
\sigma(\mathbf{z})_j = \frac{e^{z_j}}{\sum_{k=1}^K e^{z_k}} \; for j\coloneqq\{1,\dots,K\}
|
|
||||||
\end{equation}
|
|
||||||
|
|
||||||
The softmax function has high similarities with the Boltzmann distribution and was first introduced in the 19$^{\textrm{th}}$ century~\cite{Boltzmann}.
|
|
||||||
|
|
||||||
|
|
||||||
\subsubsection{Cross Entropy Loss}
|
|
||||||
Cross Entropy Loss is a well established loss function in machine learning.
|
|
||||||
Equation~\eqref{eq:crelformal}\cite{crossentropy} shows the formal general definition of the Cross Entropy Loss.
|
|
||||||
And equation~\eqref{eq:crelbinary} is the special case of the general Cross Entropy Loss for binary classification tasks.
|
|
||||||
|
|
||||||
\begin{align}
|
|
||||||
H(p,q) &= -\sum_{x\in\mathcal{X}} p(x)\, \log q(x)\label{eq:crelformal}\\
|
|
||||||
H(p,q) &= - (p \log q + (1-p) \log(1-q))\label{eq:crelbinary}\\
|
|
||||||
\mathcal{L}(p,q) &= - \frac1N \sum_{i=1}^{\mathcal{B}} (p_i \log q_i + (1-p_i) \log(1-q_i))\label{eq:crelbinarybatch}
|
|
||||||
\end{align}
|
|
||||||
|
|
||||||
Equation~$\mathcal{L}(p,q)$~\eqref{eq:crelbinarybatch}\cite{handsonaiI} is the Binary Cross Entropy Loss for a batch of size $\mathcal{B}$ and used for model training in this Practical Work.
|
|
||||||
|
|
||||||
\subsubsection{Mathematical modeling of problem}\label{subsubsec:mathematicalmodeling}
|
|
||||||
@ -1,37 +0,0 @@
|
|||||||
%! Author = lukas
|
|
||||||
%! Date = 4/9/24
|
|
||||||
|
|
||||||
@InProceedings{crossentropy,
|
|
||||||
ISSN = {00359246},
|
|
||||||
URL = {http://www.jstor.org/stable/2984087},
|
|
||||||
abstract = {This paper deals first with the relationship between the theory of probability and the theory of rational behaviour. A method is then suggested for encouraging people to make accurate probability estimates, a connection with the theory of information being mentioned. Finally Wald's theory of statistical decision functions is summarised and generalised and its relation to the theory of rational behaviour is discussed.},
|
|
||||||
author = {I. J. Good},
|
|
||||||
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
|
|
||||||
number = {1},
|
|
||||||
pages = {107--114},
|
|
||||||
publisher = {[Royal Statistical Society, Wiley]},
|
|
||||||
title = {Rational Decisions},
|
|
||||||
urldate = {2024-05-23},
|
|
||||||
volume = {14},
|
|
||||||
year = {1952}
|
|
||||||
}
|
|
||||||
|
|
||||||
@misc{efficientADpaper,
|
|
||||||
title={EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies},
|
|
||||||
author={Kilian Batzner and Lars Heckler and Rebecca König},
|
|
||||||
year={2024},
|
|
||||||
eprint={2303.14535},
|
|
||||||
archivePrefix={arXiv},
|
|
||||||
primaryClass={cs.CV},
|
|
||||||
url={https://arxiv.org/abs/2303.14535},
|
|
||||||
}
|
|
||||||
|
|
||||||
@misc{patchcorepaper,
|
|
||||||
title={Towards Total Recall in Industrial Anomaly Detection},
|
|
||||||
author={Karsten Roth and Latha Pemula and Joaquin Zepeda and Bernhard Schölkopf and Thomas Brox and Peter Gehler},
|
|
||||||
year={2022},
|
|
||||||
eprint={2106.08265},
|
|
||||||
archivePrefix={arXiv},
|
|
||||||
primaryClass={cs.CV},
|
|
||||||
url={https://arxiv.org/abs/2106.08265},
|
|
||||||
}
|
|
||||||
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