[HTML][HTML] A survey on few-shot class-incremental learning
Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …
A comprehensive survey of forgetting in deep learning beyond continual learning
Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …
While the existing surveys on forgetting have primarily focused on continual learning …
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 …
Ranpac: Random projections and pre-trained models for continual learning
MD McDonnell, D Gong, A Parvaneh… - Advances in …, 2024 - proceedings.neurips.cc
Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …
Ctp: Towards vision-language continual pretraining via compatible momentum contrast and topology preservation
Abstract Vision-Language Pretraining (VLP) has shown impressive results on diverse
downstream tasks by offline training on large-scale datasets. Regarding the growing nature …
downstream tasks by offline training on large-scale datasets. Regarding the growing nature …
Generative Multi-modal Models are Good Class Incremental Learners
X Cao, H Lu, L Huang, X Liu… - Proceedings of the …, 2024 - openaccess.thecvf.com
In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting
caused by the classifier's bias towards the current task has long posed a significant …
caused by the classifier's bias towards the current task has long posed a significant …
Expandable subspace ensemble for pre-trained model-based class-incremental learning
Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …
new classes without forgetting. Despite the strong performance of Pre-Trained Models …
Fecam: Exploiting the heterogeneity of class distributions in exemplar-free continual learning
Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …
Continuous transfer of neural network representational similarity for incremental learning
The incremental learning paradigm in machine learning has consistently been a focus of
academic research. It is similar to the way in which biological systems learn, and reduces …
academic research. It is similar to the way in which biological systems learn, and reduces …
Cat: Balanced continual graph learning with graph condensation
Continual graph learning (CGL) is purposed to continuously update a graph model with
graph data being fed in a streaming manner. Since the model easily forgets previously …
graph data being fed in a streaming manner. Since the model easily forgets previously …