FRIDA -- Generative Feature Replay for Incremental Domain Adaptation

• URL: http://arxiv.org/abs/2112.14316v1
• Date: Tue, 28 Dec 2021 22:24:32 GMT
• Title: FRIDA -- Generative Feature Replay for Incremental Domain Adaptation
• Authors: Sayan Rakshit, Anwesh Mohanty, Ruchika Chavhan, Biplab Banerjee, Gemma Roig, Subhasis Chaudhuri
• Abstract summary: We propose a novel framework called Feature based Incremental Domain Adaptation (FRIDA) For domain alignment, we propose a simple extension of the popular domain adversarial neural network (DANN) called DANN-IB. Experiment results on Office-Home, Office-CalTech, and DomainNet datasets confirm that FRIDA maintains superior stability-plasticity trade-off than the literature.
• Score: 34.00059350161178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. We assume that a labeled source domain and different unlabeled target domains are incrementally observed with the constraint that data corresponding to the current domain is only available at a time. The goal is to preserve the accuracies for all the past domains while generalizing well for the current domain. The IDA setup suffers due to the abrupt differences among the domains and the unavailability of past data including the source domain. Inspired by the notion of generative feature replay, we propose a novel framework called Feature Replay based Incremental Domain Adaptation (FRIDA) which leverages a new incremental generative adversarial network (GAN) called domain-generic auxiliary classification GAN (DGAC-GAN) for producing domain-specific feature representations seamlessly. For domain alignment, we propose a simple extension of the popular domain adversarial neural network (DANN) called DANN-IB which encourages discriminative domain-invariant and task-relevant feature learning. Experimental results on Office-Home, Office-CalTech, and DomainNet datasets confirm that FRIDA maintains superior stability-plasticity trade-off than the literature.

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