1 citations · 2 across the 10 of their papers we have counts for
10 papers
Multivariate Prototype Representation for Domain-Generalized Incremental Learning
Can Peng, Piotr Koniusz, Kaiyu Guo +2
Deep learning models suffer from catastrophic forgetting when being fine-tuned with samples of new classes. This issue becomes even more pronounced when faced with the domain shift…
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
Milad Ramezani, Liang Wang, Joshua Knights +3
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition ta…
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space
Kaushik Roy, Peyman Moghadam, Mehrtash Harandi
The performance of a lifelong learning (L3) model degrades when it is trained on a series of tasks, as the geometrical formation of the embedding space changes while learning novel…
Subspace Distillation for Continual Learning
Kaushik Roy, Christian Simon, Peyman Moghadam +1
An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel…
Deep Robust Multi-Robot Re-localisation in Natural Environments
Milad Ramezani, Ethan Griffiths, Maryam Haghighat +2
The success of re-localisation has crucial implications for the practical deployment of robots operating within a prior map or relative to one another in real-world scenarios. Usin…
Exploiting Field Dependencies for Learning on Categorical Data
Zhibin Li, Piotr Koniusz, Lu Zhang +2
Traditional approaches for learning on categorical data underexploit the dependencies between columns (\aka fields) in a dataset because they rely on the embedding of data points d…