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The main challenge in continual learning for generative models is toeffectively learn new target modes with limited samples while preservingpreviously learned ones. To this end, we introduce a new continual learningapproach for conditional generative adversarial networks by leveraging amode-affinity score specifically designed for generative modeling. First, thegenerator produces samples of existing modes for subsequent replay. Thediscriminator is then used to compute the mode similarity measure, whichidentifies a set of closest existing modes to the target. Subsequently, a labelfor the target mode is generated and given as a weighted average of the labelswithin this set. We extend the continual learning model by training it on thetarget data with the newly-generated label, while performing memory replay tomitigate the risk of catastrophic forgetting. Experimental results on benchmarkdatasets demonstrate the gains of our continual learning approach over thestate-of-the-art methods, even when using fewer training samples.

Mode-Aware Continual Learning for Conditional Generative Adversarial Networks