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

4
experimentalresults.typ
View File

@ -64,8 +64,8 @@ Which is an result that is unexpected (since one can think more samples perform
Clearly all four graphs show that the performance decreases with an increasing number of good samples.
So the conclusion is that the Few-Shot learner should always be trained with as balanced classes as possible.
== How does the 3 (ResNet, CAML, P>M>F) 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, P>M>F) 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#todo[Maybe remove comparion?]?_
@comparisonnormal shows graphs comparing the performance of the ResNet, CAML and P>M>F methods in detecting the anomaly class only including the good class as well as excluding the good class.