122 lines
5.8 KiB
XML
122 lines
5.8 KiB
XML
= Material and Methods
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== Material
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=== MVTec AD
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MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection.
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It contains over 5000 high-resolution images divided into fifteen different object and texture categories.
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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.
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// 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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== Methods
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=== 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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=== Generalisation from few samples}
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=== Patchcore}
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%todo also show values how they perform on MVTec AD
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=== 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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=== Jupyter Notebook
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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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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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=== 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.
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On each pixel a dot product creates a feature map.
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Convolutional layers capture features like edges, textures or shapes.
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Pooling layers sample down the feature maps created by the convolutional layers.
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This helps reducing the computational complexity of the overall network and help with overfitting.
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Common pooling layers include average- and max pooling.
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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.
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@cnnarchitecture shows a typical binary classification task.
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#cite(<cnnintro>)
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#figure(
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image("rsc/cnn_architecture.png", width: 80%),
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caption: [Architecture convolutional neural network. #cite(<cnnarchitectureimg>)],
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) <cnnarchitecture>
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=== RESNet
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Residual neural networks are a special type of neural network architecture.
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They are especially good for deep learning and have been used in many state-of-the-art computer vision tasks.
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The main idea behind ResNet is the skip connection.
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The skip connection is a direct connection from one layer to another layer which is not the next layer.
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This helps to avoid the vanishing gradient problem and helps with the training of very deep networks.
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ResNet has proven to be very successful in many computer vision tasks and is used in this practical work for the classification task.
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There are several different ResNet architectures, the most common are ResNet-18, ResNet-34, ResNet-50, ResNet-101 and ResNet-152. #cite(<resnet>)
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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.
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=== CAML
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Todo
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=== P$>$M$>$F
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Todo
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=== Softmax
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The Softmax function @softmax #cite(<liang2017soft>) converts $n$ numbers of a vector into a probability distribution.
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Its a generalization of the Sigmoid function and often used as an Activation Layer in neural networks.
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$
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sigma(bold(z))_j = (e^(z_j)) / (sum_(k=1)^k e^(z_k)) "for" j=(1,...,k)
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$ <softmax>
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The softmax function has high similarities with the Boltzmann distribution and was first introduced in the 19th century #cite(<Boltzmann>).
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=== Cross Entropy Loss
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Cross Entropy Loss is a well established loss function in machine learning.
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Equation~\eqref{eq:crelformal}\cite{crossentropy} shows the formal general definition of the Cross Entropy Loss.
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And equation~\eqref{eq:crelbinary} is the special case of the general Cross Entropy Loss for binary classification tasks.
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$
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H(p,q) &= -sum_(x in cal(X)) p(x) log q(x)\
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H(p,q) &= -p log(q) + (1-p) log(1-q)\
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cal(L)(p,q) &= -1/N sum_(i=1)^(cal(B)) (p_i log(q_i) + (1-p_i) log(1-q_i))
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$
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Equation~$cal(L)(p,q)$~\eqref{eq:crelbinarybatch}\cite{handsonaiI} is the Binary Cross Entropy Loss for a batch of size $cal(B)$ and used for model training in this Practical Work.
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=== Mathematical modeling of problem
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