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...
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| Materialtyp: | Online |
| Språk: | engelska |
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KIT Scientific Publishing
2024
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| Länkar: | https://library.oapen.org/handle/20.500.12657/94140 |
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| _version_ | 1869531233245986816 |
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| author | Kalb, Tobias Michael |
| author_browse | Kalb, Tobias Michael |
| author_facet | Kalb, Tobias Michael |
| author_sort | Kalb, Tobias Michael |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-147393 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1473932025-05-27T05:10:05Z Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving Kalb, Tobias Michael Automated Driving; Semantic Segmentation; Catastrophic Forgetting; Continual Learning; Deep Learning; Automatisiertes Fahren; Semantische Segmentierung; Katastrophales Vergessen; Kontinuierliches Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists 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. 2024-11-01T04:06:12Z 2024-11-01T04:06:12Z 2024-10-31T14:03:26Z 2024 book https://library.oapen.org/handle/20.500.12657/94140 9783731513735 https://directory.doabooks.org/handle/20.500.12854/147393 eng Karlsruher Schriften zur Anthropomatik open access image/jpeg image/jpeg image/jpeg image/jpeg Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/94140/1/principles-of-catastrophic-forgetting-for-continual-semantic-segmentation-in-automated-driving.pdf https://library.oapen.org/bitstream/20.500.12657/94140/1/principles-of-catastrophic-forgetting-for-continual-semantic-segmentation-in-automated-driving.pdf https://library.oapen.org/bitstream/20.500.12657/94140/1/principles-of-catastrophic-forgetting-for-continual-semantic-segmentation-in-automated-driving.pdf https://library.oapen.org/bitstream/20.500.12657/94140/1/principles-of-catastrophic-forgetting-for-continual-semantic-segmentation-in-automated-driving.pdf KIT Scientific Publishing 10.5445/KSP/1000171902 10.5445/KSP/1000171902 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513735 AG Universitätsverlage 236 open access |
| spellingShingle | Automated Driving; Semantic Segmentation; Catastrophic Forgetting; Continual Learning; Deep Learning; Automatisiertes Fahren; Semantische Segmentierung; Katastrophales Vergessen; Kontinuierliches Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists Kalb, Tobias Michael Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving |
| title | Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving |
| title_full | Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving |
| title_fullStr | Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving |
| title_full_unstemmed | Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving |
| title_short | Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving |
| title_sort | principles of catastrophic forgetting for continual semantic segmentation in automated driving |
| topic | Automated Driving; Semantic Segmentation; Catastrophic Forgetting; Continual Learning; Deep Learning; Automatisiertes Fahren; Semantische Segmentierung; Katastrophales Vergessen; Kontinuierliches Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists |
| topic_facet | Automated Driving; Semantic Segmentation; Catastrophic Forgetting; Continual Learning; Deep Learning; Automatisiertes Fahren; Semantische Segmentierung; Katastrophales Vergessen; Kontinuierliches Lernen thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists |
| url | https://library.oapen.org/handle/20.500.12657/94140 |
| work_keys_str_mv | AT kalbtobiasmichael principlesofcatastrophicforgettingforcontinualsemanticsegmentationinautomateddriving |