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fix some errors
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lukas-heiligenbrunner
2025-01-24 19:51:55 +01:00
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introduction.typ
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@ -6,7 +6,7 @@ Anomaly detection has especially in the industrial and automotive field essentia
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.
Most of the time the error rate is sub $.1%$ and therefore plenty of good data and almost no faulty data is available.
So the train data is heavily unbalaned.~#cite(<parnami2022learningexamplessummaryapproaches>)
So the train data is heavily unbalanced.~#cite(<parnami2022learningexamplessummaryapproaches>)
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.
@ -25,8 +25,8 @@ How does it compare to well established algorithms such as Patchcore or Efficien
=== How does disbalancing the Shot number affect performance?
_Does giving the Few-Shot learner more good than bad samples improve the model performance?_
=== 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 do the 3 (ResNet, CAML, \pmf) methods perform in only detecting the anomaly class?
_How much does the performance improve by only detecting the presence of an anomaly?
How does it compare to PatchCore and EfficientAD?_
#if inwriting [