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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving (Karlsruher Schriften zur Anthropomatik) - Softcover
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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.
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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Published by
Karlsruher Institut Für Technologie Okt 2024, 2024
ISBN 10: 3731513730
ISBN 13: 9783731513735
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 236 pp. Englisch. Seller Inventory # 9783731513735
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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Published by
Karlsruher Institut Für Technologie, 2024
ISBN 10: 3731513730
ISBN 13: 9783731513735
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783731513735
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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Published by
Karlsruher Institut Für Technologie Okt 2024, 2024
ISBN 10: 3731513730
ISBN 13: 9783731513735
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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. 236 pp. Englisch. Seller Inventory # 9783731513735
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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Published by
Karlsruher Institut für Technologie, 2024
ISBN 10: 3731513730
ISBN 13: 9783731513735
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving | Tobias Michael Kalb | Taschenbuch | Englisch | 2024 | Karlsruher Institut für Technologie | EAN 9783731513735 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 130291937
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Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
Published by
Karlsruher Institut für Technologie, 2024
ISBN 10: 3731513730
ISBN 13: 9783731513735
Used
Softcover
Seller: Buchpark, Trebbin, Germany
Seller rating 5 out of 5 stars
Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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. Seller Inventory # 43077902/1