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We tackle the novel problem of incremental unsupervised domain adaptation(IDA) in this paper. We assume that a labeled source domain and differentunlabeled target domains are incrementally observed with the constraint thatdata corresponding to the current domain is only available at a time. The goalis to preserve the accuracies for all the past domains while generalizing wellfor the current domain. The IDA setup suffers due to the abrupt differencesamong the domains and the unavailability of past data including the sourcedomain. Inspired by the notion of generative feature replay, we propose a novelframework called Feature Replay based Incremental Domain Adaptation (FRIDA)which leverages a new incremental generative adversarial network (GAN) calleddomain-generic auxiliary classification GAN (DGAC-GAN) for producingdomain-specific feature representations seamlessly. For domain alignment, wepropose a simple extension of the popular domain adversarial neural network(DANN) called DANN-IB which encourages discriminative domain-invariant andtask-relevant feature learning. Experimental results on Office-Home,Office-CalTech, and DomainNet datasets confirm that FRIDA maintains superiorstability-plasticity trade-off than the literature.

FRIDA -- Generative Feature Replay for Incremental Domain Adaptation