lukas/bachelor-thesis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Releases Activity

fix some errors
All checks were successful
Build Typst document / build_typst_documents (push) Successful in 1m34s
Details

Browse Source
This commit is contained in:
lukas-heiligenbrunner
2025-01-24 19:51:55 +01:00
parent 8f28a8c387
commit 71bdb0a207
3 changed files with 11 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
materialandmethods.typ
View File

@ -36,7 +36,7 @@ The bottle category contains 3 different defect classes: _broken_large_, _broken
Whereas cable has a lot more defect classes: _bent_wire_, _cable_swap_, _combined_, _cut_inner_insulation_,
_cut_outer_insulation_, _missing_cable_, _missing_wire_, _poke_insulation_.
So many more defect classes are already an indication that a classification task might be more difficult for the cable category.
More defect classes are already an indication that a classification task might be more difficult for the cable category.
#subpar.grid(
figure(image("rsc/mvtec/cable/bent_wire_example.png"), caption: [
@ -79,7 +79,7 @@ So the model is prone to overfitting to the few training samples and this means
Typically a few-shot leaning task consists of a support and query set.
Where the support-set contains the training data and the query set the evaluation data for real world evaluation.
A common way to format a few-shot leaning problem is using n-way k-shot notation.
For Example 3 target classes and 5 samples per class for training might be a 3-way 5-shot few-shot classification problem.~@snell2017prototypicalnetworksfewshotlearning @patchcorepaper
For Example, 3 target classes and 5 samples per class for training might be a 3-way 5-shot few-shot classification problem.~@snell2017prototypicalnetworksfewshotlearning @patchcorepaper
A classical example of how such a model might work is a prototypical network.
These models learn a representation of each class in a reduced dimensionality and classify new examples based on proximity to these representations in an embedding space.~@snell2017prototypicalnetworksfewshotlearning
@ -127,10 +127,10 @@ $ <crel>
Equation~$cal(L)(p,q)$ @crelbatched #cite(<handsonaiI>) is the Binary Cross Entropy Loss for a batch of size $cal(B)$ and used for model training in this Practical Work.
=== Cosine Similarity
To measure the distance between two vectors some common distance measures are used.
One popular of them is the Cosine Similarity (@cosinesimilarity).
It measures the cosine of the angle between two vectors.
The Cosine Similarity is especially useful when the magnitude of the vectors is not important.
Cosine similarity is a widely used metric for measuring the similarity between two vectors. (@cosinesimilarity).
It computes the cosine of the angle between the vectors, offering a measure of their alignment.
This property makes the cosine similarity particularly effective in scenarios where the
direction of the vector holds more important information than the magnitude.
$
cos(theta) &:= (A dot B) / (||A|| dot ||B||)\