Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving

Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for...

Celý popis

Uložené v:
Podrobná bibliografia
Hlavný autor: Kalb, Tobias Michael
Médium: E-kniha
Jazyk:English
Vydavateľské údaje: KIT Scientific Publishing 2024
Edícia:Karlsruher Schriften zur Anthropomatik
Predmet:
Automated Driving
Automatisiertes Fahren
Catastrophic Forgetting
Computer science
Computing and Information Technology
Continual Learning
Deep Learning
Katastrophales Vergessen
Kontinuierliches Lernen
Mathematical theory of computation
Maths for computer scientists
Semantic Segmentation
Semantische Segmentierung
ISBN:3731513730, 9783731513735
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Deep learning excels at extracting complex patterns but faces catastrophic forgetting when fine-tuned on new data. This book investigates how class- and domain-incremental learning affect neural networks for automated driving, identifying semantic shifts and feature changes as key factors. Tools for quantitatively measuring forgetting are selected and used to show how strategies like image augmentation, pretraining, and architectural adaptations mitigate catastrophic forgetting.
ISBN:3731513730
9783731513735
DOI:10.5445/KSP/1000171902