Continual Learning
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Benchmarks
ASC (19 tasks)
Cifar100 (20 tasks)
Tiny-ImageNet (10tasks)
F-CelebA (10 tasks)
20Newsgroup (10 tasks)
DSC (10 tasks)
Cifar100 (10 tasks)
ImageNet-50 (5 tasks)
Permuted MNIST
split CIFAR-100
5-Datasets
5-dataset - 1 epoch
AIDS
Coarse-CIFAR100
MLT17
Rotated MNIST
Split CIFAR-10 (5 tasks)
Split MNIST (5 tasks)
miniImagenet
Most implemented
Overcoming catastrophic forgetting in neural networks
Learning without Forgetting
Progressive Neural Networks
Continual learning with hypernetworks
Three scenarios for continual learning
Variational Continual Learning
Papers
MePo++: Unifying Representation Refinement and Reconciliation for General Continual Learning
General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous data, making it a realistic yet challenging setting for continual int…
Continual LearningEfficient Online Continual Foundation Model Fine-Tuning for Predictive Process Monitoring
Predictive Process Monitoring (PPM) models are increasingly deployed in dynamic environments where concept drift causes the underlying process distribution to shift over time. While recent work has moved toward online co…
Continual LearningThomson: Continual Learning of Frontier Models for SovereignAI
The development of frontier models is commonly perceived to be the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetry between developers and the diverse use…
Continual LearningPrompt EngineeringDomain AdaptationUnifying Detection and Adaptation in Task-Free Continual Learning
To mitigate catastrophic forgetting in downstream continual learning (CL) for large language models (LLMs), existing methods typically constrain parameter updates or introduce task-specific adaptation modules. However, t…
Continual LearningGeo-LoRA: Geometry-Aware Subspace Evolution for Low-Rank Adaptation in Continual Learning
Rehearsal-free class-incremental learning (CIL) with LoRA adapters remains challenging because the low-rank subspaces updated across tasks evolve without geometric control, causing unstable shared representations and rep…
class-incremental learningContinual LearningParameter Efficient Continual Learning for Sparse Event-Based Transformers
Robotic and edge intelligence systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previously learned knowledge under strict memory and energy constraints. W…
parameter-efficient fine-tuningclass-incremental learningContinual LearningEvent-based vision