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Gradient Regularized Contrastive Learning for Continual Domain Adaptation
[article]
2021
arXiv
pre-print
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algorithms remains a challenge. To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains. The obstacles in this problem are both domain shift and catastrophic forgetting. We propose Gradient
arXiv:2103.12294v1
fatcat:kycqokas6rahph2u4adpql2fmy