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A comprehensive survey of continual learning: Theory, method and application
To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
[PDF][PDF] Deep class-incremental learning: A survey
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
A model or 603 exemplars: Towards memory-efficient class-incremental learning
Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …
Modular deep learning
Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …
trained models fine-tuned for downstream tasks achieve better performance with fewer …
Catastrophic forgetting in deep learning: a comprehensive taxonomy
Deep Learning models have achieved remarkable performance in tasks such as image
classification or generation, often surpassing human accuracy. However, they can struggle …
classification or generation, often surpassing human accuracy. However, they can struggle …
New insights on reducing abrupt representation change in online continual learning
In the online continual learning paradigm, agents must learn from a changing distribution
while respecting memory and compute constraints. Experience Replay (ER), where a small …
while respecting memory and compute constraints. Experience Replay (ER), where a small …
Class-incremental learning: A survey
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
Learnability and algorithm for continual learning
This paper studies the challenging continual learning (CL) setting of Class Incremental
Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or …
Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or …
Towards modular llms by building and reusing a library of loras
The growing number of parameter-efficient adaptations of a base large language model
(LLM) calls for studying whether we can reuse such trained adapters to improve …
(LLM) calls for studying whether we can reuse such trained adapters to improve …
Dynamically expandable graph convolution for streaming recommendation
Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …
information overload and satisfy users' diverse needs. However, conventional …