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Found 3 papers, 1 papers with code
Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How
Recent years have seen considerable progress in the continual training of deep neural networks, predominantly thanks to approaches that add replay or regularization terms to the loss function to approximate the joint loss over all tasks so far.
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting
Carefully taking both aspects into account, we show that, even though it is true that feature forgetting can be small in absolute terms, newly learned information tends to be forgotten just as catastrophically at the level of the representation as it is at the output level.
A Procedural World Generation Framework for Systematic Evaluation of Continual Learning
Several families of continual learning techniques have been proposed to alleviate catastrophic interference in deep neural network training on non-stationary data.
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