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Found 4 papers, 0 papers with code
Online Continual Learning via the Knowledge Invariant and Spread-out Properties
However, a key challenge in this continual learning paradigm is catastrophic forgetting, namely adapting a model to new tasks often leads to severe performance degradation on prior tasks.
Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation
In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA).
Selecting Related Knowledge via Efficient Channel Attention for Online Continual Learning
Based on this fact, in this paper we propose a new framework, named Selecting Related Knowledge for Online Continual Learning (SRKOCL), which incorporates an additional efficient channel attention mechanism to pick the particular related knowledge for every task.
Bilevel Online Deep Learning in Non-stationary Environment
In this paper, we proposed a new Bilevel Online Deep Learning (BODL) framework, which combine bilevel optimization strategy and online ensemble classifier.
