Search Results for author: Matthias De Lange

Found 9 papers, 7 papers with code

CLAD: A realistic Continual Learning benchmark for Autonomous Driving

1 code implementation7 Oct 2022 Eli Verwimp, Kuo Yang, Sarah Parisot, Hong Lanqing, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.

Autonomous Driving Continual Learning +2

Continual evaluation for lifelong learning: Identifying the stability gap

1 code implementation26 May 2022 Matthias De Lange, Gido van de Ven, Tinne Tuytelaars

Contemporary evaluation protocols and metrics in continual learning are task-based and quantify the trade-off between stability and plasticity only at task transitions.

Continual Learning

Rehearsal revealed: The limits and merits of revisiting samples in continual learning

1 code implementation ICCV 2021 Eli Verwimp, Matthias De Lange, Tinne Tuytelaars

Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research.

Continual Learning

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

1 code implementation CVPR 2020 Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.

Continual Learning Domain Adaptation +2

A continual learning survey: Defying forgetting in classification tasks

1 code implementation18 Sep 2019 Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.

Classification Continual Learning +2

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