Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Ranpac: Random projections and pre-trained models for continual learning
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 …
Few-shot class-incremental learning via training-free prototype calibration
Real-world scenarios are usually accompanied by continuously appearing classes with
scare labeled samples, which require the machine learning model to incrementally learn …
scare labeled samples, which require the machine learning model to incrementally learn …
Generative Multi-modal Models are Good Class Incremental Learners
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 …
Few-shot class incremental learning with attention-aware self-adaptive prompt
Abstract Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn
new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL …
new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL …
Continual learning with pre-trained models: A survey
Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …
A Practitioner's Guide to Continual Multimodal Pretraining
Multimodal foundation models serve numerous applications at the intersection of vision and
language. Still, despite being pretrained on extensive data, they become outdated over time …
language. Still, despite being pretrained on extensive data, they become outdated over time …
Class-incremental learning with clip: Adaptive representation adjustment and parameter fusion
Class-incremental learning is a challenging problem, where the goal is to train a model that
can classify data from an increasing number of classes over time. With the advancement of …
can classify data from an increasing number of classes over time. With the advancement of …