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Rahaf Aljundi

Alumni

Publications

Controlling Forgetting with Test-Time Data in Continual Learning
Vaibhav Singh
Rahaf Aljundi
Eugene Belilovsky
Foundational vision-language models excel in various tasks but require updates as new tasks or domains emerge. Current Continual Learning (C… (see more)L) methods, which focus on supervised training, often suffer from significant forgetting, performing worse than the original models in zero-shot scenarios. This work proposes leveraging test-time, unsupervised data in a self-supervised manner to refresh the model’s memory of previously learned tasks, minimizing forgetting without additional labeling. By introducing a student-teacher framework with gradient-based sparse parameter updates, the approach enhances performance on prior tasks and reduces reliance on offline memory buffers, effectively improving continual learning outcomes.
2024-10-10
NeurIPS.cc/2024/Workshop/AFM (poster)
openreview.net
openreview.net
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Nader Asadi
MohammadReza Davari
Sudhir Mudur
Rahaf Aljundi
Eugene Belilovsky
In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent… (see more) best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be restrictive in many real-world scenarios, for example when task data is sensitive or proprietary. To overcome the necessity of using previous tasks' data, in this work, we start with strong representation learning methods that have been shown to be less prone to forgetting. We propose a holistic approach to jointly learn the representation and class prototypes while maintaining the relevance of old class prototypes and their embedded similarities. Specifically, samples are mapped to an embedding space where the representations are learned using a supervised contrastive loss. Class prototypes are evolved continually in the same latent space, enabling learning and prediction at any point. To continually adapt the prototypes without keeping any prior task data, we propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data. This method yields state-of-the-art performance in the task-incremental setting, outperforming methods relying on large amounts of data, and provides strong performance in the class-incremental setting without using any stored data points.
2023-01-01
ICML (published)
doi.org
openreview.net
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
MohammadReza Davari
Nader Asadi
Sudhir Mudur
Rahaf Aljundi
Eugene Belilovsky
Continual Learning (CL) research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic fo… (see more)rgetting is associated with an abrupt loss of knowledge previously learned by a model when the task, or more broadly the data distribution, being trained on changes. In supervised learning problems this forgetting, resulting from a change in the model's representation, is typically measured or observed by evaluating the decrease in old task performance. However, a model's representation can change without losing knowledge about prior tasks. In this work we consider the concept of representation forgetting, observed by using the difference in performance of an optimal linear classifier before and after a new task is introduced. Using this tool we revisit a number of standard continual learning benchmarks and observe that, through this lens, model representations trained without any explicit control for forgetting often experience small representation forgetting and can sometimes be comparable to methods which explicitly control for forgetting, especially in longer task sequences. We also show that representation forgetting can lead to new insights on the effect of model capacity and loss function used in continual learning. Based on our results, we show that a simple yet competitive approach is to learn representations continually with standard supervised contrastive learning while constructing prototypes of class samples when queried on old samples.11The code to reproduce our results is publicly available at: https://github.com/rezazzr/Probing-Representation-Forgetting
2022-06-18
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
arxiv.org
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
MohammadReza Davari
Nader Asadi
Sudhir Mudur
Rahaf Aljundi
Eugene Belilovsky
Continual Learning (CL) research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic fo… (see more)rgetting is associated with an abrupt loss of knowledge previously learned by a model when the task, or more broadly the data distribution, being trained on changes. In supervised learning problems this forgetting, resulting from a change in the model's representation, is typically measured or observed by evaluating the decrease in old task performance. However, a model's representation can change without losing knowledge about prior tasks. In this work we consider the concept of representation forgetting, observed by using the difference in performance of an optimal linear classifier before and after a new task is introduced. Using this tool we revisit a number of standard continual learning benchmarks and observe that, through this lens, model representations trained without any explicit control for forgetting often experience small representation forgetting and can sometimes be comparable to methods which explicitly control for forgetting, especially in longer task sequences. We also show that representation forgetting can lead to new insights on the effect of model capacity and loss function used in continual learning. Based on our results, we show that a simple yet competitive approach is to learn representations continually with standard supervised contrastive learning while constructing prototypes of class samples when queried on old samples.11The code to reproduce our results is publicly available at: https://github.com/rezazzr/Probing-Representation-Forgetting
2022-03-24
ArXiv (preprint)
doi.org
arxiv.org
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia
Rahaf Aljundi
Nader Asadi
Tinne Tuytelaars
Joelle Pineau
Eugene Belilovsky
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. E… (see more)xperience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes’ representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks.
2022-01-28
ICLR.cc/2022/Conference (poster)
doi.org
openreview.net
Online Continual Learning with Maximally Interfered Retrieval
Rahaf Aljundi
Lucas Caccia
Eugene Belilovsky
Massimo Caccia
Min Lin
Laurent Charlin
Tinne Tuytelaars
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for mo… (see more)dern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.
2019-08-11
ArXiv (preprint)
arxiv.org
arxiv.org
Online continual learning with no task boundaries
Rahaf Aljundi
Min Lin
Baptiste Goujaud
Yoshua Bengio
Continual learning is the ability of an agent to learn online with a non-stationary and never-ending stream of data. A key component for suc… (see more)h never-ending learning process is to overcome the catastrophic forgetting of previously seen data, a problem that neural networks are well known to suffer from. The solutions developed so far often relax the problem of continual learning to the easier task-incremental setting, where the stream of data is divided into tasks with clear boundaries. In this paper, we break the limits and move to the more challenging online setting where we assume no information of tasks in the data stream. We start from the idea that each learning step should not increase the losses of the previously learned examples through constraining the optimization process. This means that the number of constraints grows linearly with the number of examples, which is a serious limitation. We develop a solution to select a fixed number of constraints that we use to approximate the feasible region defined by the original constraints. We compare our approach against the methods that rely on task boundaries to select a fixed set of examples, and show comparable or even better results, especially when the boundaries are blurry or when the data distributions are imbalanced.
2019-03-20
arXiv.org (preprint)
dblp.uni-trier.de
Gradient based sample selection for online continual learning
Rahaf Aljundi
Min Lin
Baptiste Goujaud
Yoshua Bengio
A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overc… (see more)ome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous works often depend on task boundary and i.i.d. assumptions to properly select samples for the replay buffer. In this work, we formulate sample selection as a constraint reduction problem based on the constrained optimization view of continual learning. The goal is to select a fixed subset of constraints that best approximate the feasible region defined by the original constraints. We show that it is equivalent to maximizing the diversity of samples in the replay buffer with parameters gradient as the feature. We further develop a greedy alternative that is cheap and efficient. The advantage of the proposed method is demonstrated by comparing to other alternatives under the continual learning setting. Further comparisons are made against state of the art methods that rely on task boundaries which show comparable or even better results for our method.
arxiv.org