add abstract, finish the alternatvie methods and fix some todos and improve sources

main.typ

@@ -52,7 +52,18 @@
   place-of-submission: "Linz",
   title: "Few shot learning for anomaly detection",
   abstract-en: [//max. 250 words
-#lorem(200)  ],
+    This thesis explores the application of Few-Shot Learning (FSL) in anomaly detection, a critical area in industrial and automotive domains requiring robust and efficient algorithms for identifying defects.
+    Traditional methods, such as PatchCore and EfficientAD, achieve high accuracy but often demand extensive training data and are sensitive to environmental changes, necessitating frequent retraining.
+    FSL offers a promising alternative by enabling models to generalize effectively from minimal samples, thus reducing training time and adaptation overhead.
+
+    The study evaluates three FSL methods—ResNet50, P>M>F, and CAML—using the MVTec AD dataset.
+    Experiments focus on tasks such as anomaly detection, class imbalance handling, and comparison of distance metrics.
+    Results indicate that while FSL methods trail behind state-of-the-art algorithms in detecting anomalies, they excel in classifying anomaly types, showcasing potential in scenarios requiring detailed defect identification.
+    Among the tested approaches, P>M>F emerged as the most robust, demonstrating superior accuracy across various settings.
+
+    This research underscores the limitations and niche applicability of FSL in anomaly detection, advocating its integration with established algorithms for enhanced performance.
+    Future work should address the scalability and domain-specific adaptability of FSL techniques to broaden their utility in industrial applications.
+  ],
   abstract-de: none,// or specify the abbstract_de in a container []
   acknowledgements: none,//acknowledgements: none // if you are self-made
   show-title-in-header: false,