- Embracing Change🔍
- Overcoming Catastrophic Forgetting Using Sparse Coding and Meta ...🔍
- Catastrophic forgetting in Lifelong learning🔍
- Privacy|Preserving Federated Learning with Consistency via ...🔍
- Alleviating catastrophic forgetting using context|dependent gating ...🔍
- Dynamic memory to alleviate catastrophic forgetting in continual ...🔍
- QUANTIFYING CATASTROPHIC FORGETTING IN CONTINUAL ...🔍
- NeurIPS 2024 Papers🔍
Consistency is the Key to Further Mitigating Catastrophic Forgetting ...
Embracing Change: Continual Learning in Deep Neural Networks
Mitigating catastrophic forgetting is often prioritized in research ... memory vectors that represent the key features of each task. Not only is this ...
Overcoming Catastrophic Forgetting Using Sparse Coding and Meta ...
Therefore, there is a need for more efficient and flexible solutions. In the context of sequential learning, previous work has followed two main ...
Catastrophic forgetting in Lifelong learning - Aman
In principle, a model should use the knowledge acquired from the previous tasks to learn a new task more effectively. It should not completely ...
Privacy-Preserving Federated Learning with Consistency via ...
To address these limitations, the researchers suggest further research into more ... mitigating catastrophic forgetting and enhancing the general ...
Alleviating catastrophic forgetting using context-dependent gating ...
... further mitigate catastrophic forgetting. Another class of studies ... The two key differences between our method and theirs are: (i) ...
Dynamic memory to alleviate catastrophic forgetting in continual ...
A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains.
Mitigation strategies – such as episodic replay, model regularization, or parameter freezing – have been proposed for Continual Learning (CL), a centralized ...
Online Consistency of the Nearest Neighbor Rule · Upping the Game: How 2D ... Key Weights Corresponding to Basic Syntactic or High-level Semantic Information ...
bibs/merged.bib · master · Alexander Gepperth / Iclr25 · GitLab
A key challenge in CL is catastrophic forgetting, which arises when performance on a previously mastered task is reduced when learning a new ...
A Continual Learning Perspective of Dynamic SLAM
We take a step further to address the problem by continually learning what to memorize and what to forget. NeRF construction in changing environments. Although ...
How Autonomous Mobile Robots (AMRs) are Revolutionizing ...
By training on this mixed dataset, the model learns to perform multiple tasks simultaneously, mitigating the issue of catastrophic forgetting— ...
Mitigating Catastrophic Forgetting in Large-Scale Models with ...
The core idea is to penalize changes to important parameters when learning new tasks. This method need more resource computation. How EWC Works.
Remembering for the Right Reasons: Explanations Reduce ...
The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting.
Catastrophic Forgetting in Neural Networks - DEV
Overfitting is one of the main factors affecting catastrophic forgetting. When fitting a more complex model to a given sample, there tends to be ...
Heterogeneous Continual Learning | CVPR 2023 - YouTube
Heterogeneous Continual Learning | CVPR 2023. 1.9K views · 1 year ago ...more ... Continual Learning and Catastrophic Forgetting. Paul Hand•14K ...
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal ...
Just as a tailor selects patches to enhance a garment, Model Tailor discerns and modifies a minimal set of parameters, known as the “model patch”, from the fine ...
Re-identification - Paper Reading
Key challenges involve addressing catastrophic forgetting caused by ... increase the learning intensity of the model for key information. A dual-length ...