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ORCIDVisvanathan Ramesh
- > Home > Persons > Visvanathan Ramesh
Publications
- 2024
[c60]Achref Jaziri, Martin Mundt, Andres Fernandez Rodriguez, Visvanathan Ramesh:
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation. WACV 2024: 8621-8631- 2023
- [j16]Martin Mundt
, Yongwon Hong, Iuliia Pliushch, Visvanathan Ramesh:
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning. Neural Networks 160: 306-336 (2023) - [i15]Achref Jaziri, Martin Mundt, Andres Fernandez Rodriguez, Visvanathan Ramesh:
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation. CoRR abs/2309.09637 (2023) - 2022
- [j15]Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Yong Won Hong, Visvanathan Ramesh:
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. J. Imaging 8(4): 93 (2022) - [c59]Iuliia Pliushch, Martin Mundt, Nicolas Lupp, Visvanathan Ramesh:
When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics. ECCV (8) 2022: 397-413 - 2021
- [c58]Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference. CVPR Workshops 2021: 3523-3532 - [c57]Timm Hess, Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning. NeurIPS Datasets and Benchmarks 2021 - [i14]Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference. CoRR abs/2104.06788 (2021) - [i13]Iuliia Pliushch, Martin Mundt, Nicolas Lupp, Visvanathan Ramesh:
When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics. CoRR abs/2105.08997 (2021) - [i12]Timm Hess, Martin Mundt, Iuliia Pliushch, Visvanathan Ramesh:
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning. CoRR abs/2106.02585 (2021) - 2020
- [i10]Martin Mundt, Yong Won Hong, Iuliia Pliushch, Visvanathan Ramesh:
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning. CoRR abs/2009.01797 (2020) - 2019
- [c56]Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh:
Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset. CVPR 2019: 11196-11205 - [c55]Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh:
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? ICCV Workshops 2019: 753-757 - [i9]Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh:
Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset. CoRR abs/1904.08486 (2019) - [i8]Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh:
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. CoRR abs/1905.12019 (2019) - [i7]Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Visvanathan Ramesh:
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? CoRR abs/1908.09625 (2019) - 2018
- [i6]Martin Mundt, Sagnik Majumder, Tobias Weis, Visvanathan Ramesh:
Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures. CoRR abs/1812.05836 (2018) - 2017
- [c53]Tobias Weis, Martin Mundt, Patrick Harding, Visvanathan Ramesh:
Anomaly detection for automotive visual signal transition estimation. ITSC 2017: 1-8 - [c52]Timm Hess, Martin Mundt, Tobias Weis, Visvanathan Ramesh:
Large-Scale Stochastic Scene Generation and Semantic Annotation for Deep Convolutional Neural Network Training in the RoboCup SPL. RoboCup 2017: 33-44 - [i4]Martin Mundt, Tobias Weis, Kishore Konda, Visvanathan Ramesh:
Building effective deep neural network architectures one feature at a time. CoRR abs/1705.06778 (2017)
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