Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? - Martin Mundt; Iuliia Pliushch; Sagnik Majumder; Visvanathan Ramesh (ICCV Workshops, 2019)

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. - Martin Mundt; Sagnik Majumder; Iuliia Pliushch; Visvanathan Ramesh (CoRR, 2019)

A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning. - Martin Mundt; Yong Won Hong; Iuliia Pliushch; Visvanathan Ramesh (CoRR, 2020)

Neural Architecture Search of Deep Priors: Towards Continual Learning Without Catastrophic Interference. - Martin Mundt; Iuliia Pliushch; Visvanathan Ramesh (CVPR Workshops, 2021)

A Procedural World Generation Framework for Systematic Evaluation of Continual Learning. - Timm Hess; Martin Mundt; Iuliia Pliushch; Visvanathan Ramesh (NeurIPS Datasets and Benchmarks, 2021)

When Deep Classifiers Agree: Analyzing Correlations Between Learning Order and Image Statistics. - Iuliia Pliushch; Martin Mundt; Nicolas Lupp; Visvanathan Ramesh (ECCV (8), 2022)

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. - Martin Mundt; Iuliia Pliushch; Sagnik Majumder; Yong Won Hong; Visvanathan Ramesh (J. Imaging, 2022)

A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning. - Martin Mundt; Yongwon Hong; Iuliia Pliushch; Visvanathan Ramesh (Neural Networks, 2023)

Representation Learning in a Decomposed Encoder Design for Bio-Inspired Hebbian Learning. - Achref Jaziri; Sina Ditzel; Iuliia Pliushch; Visvanathan Ramesh (ECCV Workshops (14), 2024)

Uncertainty-Aware Decomposed Hybrid Networks. - Sina Ditzel; Achref Jaziri; Iuliia Pliushch; Visvanathan Ramesh (CoRR, 2025)