Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning
Online class-incremental continual learning (CL) studies the problem of learning new
classes continually from an online non-stationary data stream, intending to adapt to new …
classes continually from an online non-stationary data stream, intending to adapt to new …
Pcr: Proxy-based contrastive replay for online class-incremental continual learning
Online class-incremental continual learning is a specific task of continual learning. It aims to
continuously learn new classes from data stream and the samples of data stream are seen …
continuously learn new classes from data stream and the samples of data stream are seen …
Not just selection, but exploration: Online class-incremental continual learning via dual view consistency
Online class-incremental continual learning aims to learn new classes continually from a
never-ending and single-pass data stream, while not forgetting the learned knowledge of old …
never-ending and single-pass data stream, while not forgetting the learned knowledge of old …
Online prototype learning for online continual learning
Online continual learning (CL) studies the problem of learning continuously from a single-
pass data stream while adapting to new data and mitigating catastrophic forgetting …
pass data stream while adapting to new data and mitigating catastrophic forgetting …
Online class-incremental continual learning with adversarial shapley value
As image-based deep learning becomes pervasive on every device, from cell phones to
smart watches, there is a growing need to develop methods that continually learn from data …
smart watches, there is a growing need to develop methods that continually learn from data …
Online continual learning with contrastive vision transformer
Online continual learning (online CL) studies the problem of learning sequential tasks from
an online data stream without task boundaries, aiming to adapt to new data while alleviating …
an online data stream without task boundaries, aiming to adapt to new data while alleviating …
Rethinking experience replay: a bag of tricks for continual learning
In Continual Learning, a Neural Network is trained on a stream of data whose distribution
shifts over time. Under these assumptions, it is especially challenging to improve on classes …
shifts over time. Under these assumptions, it is especially challenging to improve on classes …
Gcr: Gradient coreset based replay buffer selection for continual learning
Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …
increasing number of tasks encountered sequentially, thereby potentially leveraging …
Dealing with cross-task class discrimination in online continual learning
Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the
only challenge. This paper argues for another challenge in class-incremental learning (CIL) …
only challenge. This paper argues for another challenge in class-incremental learning (CIL) …
Cba: Improving online continual learning via continual bias adaptor
Online continual learning (CL) aims to learn new knowledge and consolidate previously
learned knowledge from non-stationary data streams. Due to the time-varying training …
learned knowledge from non-stationary data streams. Due to the time-varying training …