Ke Deng

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Michael Botsch

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Publications

2023
[j61]
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Lakshman Balasubramanian, Jonas Wurst, Michael Botsch, Ke Deng:
Open-World Learning for Traffic Scenarios Categorisation. IEEE Trans. Intell. Veh. 8(5): 3506-3521 (2023)
[c88]
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Lakshman Balasubramanian, Jonas Wurst, Robin Egolf, Michael Botsch, Wolfgang Utschick, Ke Deng:
SceneDiffusion: Conditioned Latent Diffusion Models for Traffic Scene Prediction. ITSC 2023: 3914-3921
2022
[c85]
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Lakshman Balasubramanian, Jonas Wurst, Robin Egolf, Michael Botsch, Wolfgang Utschick, Ke Deng:
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios. ITSC 2022: 1471-1478
[i13]
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  - journals/corr/abs-2207-08609
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Lakshman Balasubramanian, Jonas Wurst, Robin Egolf, Michael Botsch, Wolfgang Utschick, Ke Deng:
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios. CoRR abs/2207.08609 (2022)
2021
[c78]
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  - conf/ivs/Balasubramanian21
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Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng:
Open-Set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios. IV 2021: 674-681
[c77]
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Lakshman Balasubramanian, Jonas Wurst, Michael Botsch, Ke Deng:
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity. IV 2021: 682-689
[i12]
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  - journals/corr/abs-2105-07635
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Lakshman Balasubramanian, Friedrich Kruber, Michael Botsch, Ke Deng:
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios. CoRR abs/2105.07635 (2021)
[i11]
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  - journals/corr/abs-2105-07639
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Lakshman Balasubramanian, Jonas Wurst, Michael Botsch, Ke Deng:
Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity. CoRR abs/2105.07639 (2021)

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