9 citations · 20 across the 4 of their papers we have counts for
13 papers
Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions
Stefan Leitner, M. Jehanzeb Mirza, Wei Lin +5
In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when te…
An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions
M. Jehanzeb Mirza, Marc Masana, Horst Possegger +1
Although deep neural networks enable impressive visual perception performance for autonomous driving, their robustness to varying weather conditions still requires attention. When…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
Disentanglement of Color and Shape Representations for Continual Learning
David Berga, Marc Masana, Joost Van de Weijer
We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjust…
On Class Orderings for Incremental Learning
Marc Masana, Bartłomiej Twardowski, Joost van de Weijer
The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incre…
Ternary Feature Masks: zero-forgetting for task-incremental learning
Marc Masana, Tinne Tuytelaars, Joost van de Weijer
We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a m…