Rahaf Aljundi | DeepAI

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∙ 08/23/2023

Overcoming General Knowledge Loss with Selective Parameter Finetuning

Foundation models encompass an extensive knowledge base and offer remark...

0 Wenxuan Zhang, et al. ∙

∙ 03/26/2023

Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning

In Continual learning (CL) balancing effective adaptation while combatin...

0 Nader Asadi, et al. ∙

∙ 03/23/2023

First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning

In Class-Incremental Learning (CIL) an image classification system is ex...

0 Aristeidis Panos, et al. ∙

∙ 03/23/2023

Calibrated Out-of-Distribution Detection with a Generic Representation

Out-of-distribution detection is a common issue in deploying vision mode...

0 Tomáš Vojíř, et al. ∙

∙ 10/10/2022

A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning

With the success of pretraining techniques in representation learning, a...

10 Paul Janson, et al. ∙

∙ 03/24/2022

Probing Representation Forgetting in Supervised and Unsupervised Continual Learning

Continual Learning research typically focuses on tackling the phenomenon...

6 MohammadReza Davari, et al. ∙

∙ 03/08/2022

New Insights on Reducing Abrupt Representation Change in Online Continual Learning

In the online continual learning paradigm, agents must learn from a chan...

3 Lucas Caccia, et al. ∙

∙ 10/04/2021

Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection

In order to robustly deploy object detectors across a wide range of scen...

1 Farzaneh Rezaeianaran, et al. ∙

∙ 06/24/2021

Continual Novelty Detection

Novelty Detection methods identify samples that are not representative o...

5 Rahaf Aljundi, et al. ∙

∙ 04/11/2021

Reducing Representation Drift in Online Continual Learning

We study the online continual learning paradigm, where agents must learn...

0 Lucas Caccia, et al. ∙

∙ 10/14/2020

Identifying Wrongly Predicted Samples: A Method for Active Learning

State-of-the-art machine learning models require access to significant a...

0 Rahaf Aljundi, et al. ∙

∙ 10/07/2019

Continual Learning in Neural Networks

Artificial neural networks have exceeded human-level performance in acco...

62 Rahaf Aljundi, et al. ∙

∙ 09/18/2019

Continual learning: A comparative study on how to defy forgetting in classification tasks

Artificial neural networks thrive in solving the classification problem ...

0 Matthias De Lange, et al. ∙

∙ 08/11/2019

Online Continual Learning with Maximally Interfered Retrieval

Continual learning, the setting where a learning agent is faced with a n...

4 Rahaf Aljundi, et al. ∙

∙ 03/20/2019

Online continual learning with no task boundaries

Continual learning is the ability of an agent to learn online with a non...

10 Rahaf Aljundi, et al. ∙

∙ 12/26/2018

Exploring the Challenges towards Lifelong Fact Learning

So far life-long learning (LLL) has been studied in relatively small-sca...

0 Mohamed Elhoseiny, et al. ∙

∙ 12/10/2018

Task-Free Continual Learning

Methods proposed in the literature towards continual deep learning typic...

0 Rahaf Aljundi, et al. ∙

∙ 06/14/2018

Selfless Sequential Learning

Sequential learning studies the problem of learning tasks in a sequence ...

0 Rahaf Aljundi, et al. ∙

∙ 11/27/2017

Memory Aware Synapses: Learning what (not) to forget

Humans can learn in a continuous manner. Old rarely utilized knowledge c...

0 Rahaf Aljundi, et al. ∙

∙ 04/06/2017

Encoder Based Lifelong Learning

This paper introduces a new lifelong learning solution where a single mo...

0 Amal Rannen Triki, et al. ∙

∙ 11/28/2016

Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images

In this work, we aim at automatically labeling actors in a TV series. Ra...

0 Rahaf Aljundi, et al. ∙

∙ 11/18/2016

Expert Gate: Lifelong Learning with a Network of Experts

In this paper we introduce a model of lifelong learning, based on a Netw...

0 Rahaf Aljundi, et al. ∙

∙ 03/23/2016

Lightweight Unsupervised Domain Adaptation by Convolutional Filter Reconstruction

End-to-end learning methods have achieved impressive results in many are...

0 Rahaf Aljundi, et al. ∙

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