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Found 3 papers, 1 papers with code
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal
Pseudo-rehearsal allows neural networks to learn a sequence of tasks without forgetting how to perform in earlier tasks.
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
We propose a model that overcomes catastrophic forgetting in sequential reinforcement learning by combining ideas from continual learning in both the image classification domain and the reinforcement learning domain.
Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks
In general, neural networks are not currently capable of learning tasks in a sequential fashion.
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