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Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay
[article]
2021
arXiv
pre-print
This paper proposes two novel knowledge transfer techniques for class-incremental learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the conventional generative replay, the generative model is pre-trained for old data and shared in extra memory for later incremental learning. In our proposed DF-GR, we train a generative model from scratch without using any training data, based on
arXiv:2106.09835v1
fatcat:bsrxstzvsrdbtb7nml2qow34bi