Example forgetting and rehearsal in continual learning

Example forgetting and rehearsal in continual learning - ScienceDirect

We propose a simple strategy for example selection: keeping the least forgettable examples according to precomputed or continually updated forgetting ...

In this work, we examine in image classification whether all training examples are forgotten equally and which ones are worth keeping in the memory.

Example forgetting and rehearsal in continual learning - NASA/ADS

A major challenge of training neural networks on different tasks in a sequential manner is catastrophic forgetting, where earlier experiences are forgotten ...

Example forgetting and rehearsal in continual learning

Download Citation | On Mar 1, 2024, Beatrix Benkő published Example forgetting and rehearsal in continual learning | Find, read and cite all ...

Continual Learning and Catastrophic Forgetting

A concrete example of catastrophic forgetting is transfer learning using a deep neural network. In a typical transfer learning setting, where the source ...

Forgetting Order of Continual Learning: Examples That are ... - arXiv

We demonstrate that replay-based continual learning methods can leverage this phenomenon by focusing on mid-learned examples for rehearsal.

GRASP: A Rehearsal Policy for Efficient Online Continual Learning

Here, we propose a new sample selection or rehearsal policy called GRASP ... forgetting of old knowledge and is difficult to correct without longer training.

Forgetting Order of Continual Learning: Examples That are ... - arXiv

We demonstrate that replay-based continual learning methods can leverage this phenomenon by focusing on mid-learned examples for rehearsal. We ...

Rehearsal-Free Continual Learning Over Small Non-I.I.D. Batches

In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never before seen classes as well as ...

A Simple but Strong Baseline for Online Continual Learning

Rehearsal-based meth- ods attempt to approximate the observed input distributions over time with a small memory and revisit them later to avoid forgetting.

(PDF) Forgetting Order of Continual Learning: Examples That are ...

We demonstrate that replay-based continual learning methods can leverage this phenomenon by focusing on mid-learned examples for rehearsal. We ...

Mitigating Catastrophic Forgetting in Large Language Models with ...

As shown in Figure 1, unlike standard rehearsal-based continual learn- ing that samples training instances from previous stages as rehearsal ...

Rehearsal-Based Methods for Continual Learning - IRIS Unimore

Indeed, when inserting an example in the rehearsal memory, we can reasonably ... continual learning survey: Defying forgetting in classification tasks.

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual...

Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously ...

ContinualAI/continual-learning-papers - GitHub

... Learning – ICANN 2019: Deep Learning, 714--728, 2019. [rnn]; An Empirical Study of Example Forgetting during Deep Neural Network Learning by Mariya Toneva ...

Condensed Composite Memory Continual Learning - IEEE Xplore

While many recently proposed methods for continual learning use some training examples for rehearsal, their performance strongly depends on the number of stored ...

The Limits and Merits of Revisiting Samples in Continual Learning

(Section 2), this study focuses and refers to rehearsal in its most direct form, i.e. by sampling the input distribution in a limited rehearsal memory from ...

Continual Learning | Papers With Code

To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. 5.

Continual Learning Beyond Catastrophic Forgetting in ... - YouTube

... continual learning in rehearsal-free and distributed/multi-agent settings. He is also the lead maintainer of Avalanche, an open-source continual ...

The limits and merits of revisiting samples in continual learning

This work hypothesize that models trained sequentially with rehearsal tend to stay in the same low-loss region after a task has finished, but are at risk of ...