  We present a continual learning approach for generative adversarial networks
(GANs), by designing and leveraging parameter-efficient feature map
transformations. Our approach is based on learning a set of global and
task-specific parameters. The global parameters are fixed across tasks whereas
the task specific parameters act as local adapters for each task, and help in
efficiently transforming the previous task's feature map to the new task's
feature map. Moreover, we propose an element-wise residual bias in the
transformed feature space which highly stabilizes GAN training. In contrast to
the recent approaches for continual GANs, we do not rely on memory replay,
regularization towards previous tasks' parameters, or expensive weight
transformations. Through extensive experiments on challenging and diverse
datasets, we show that the feature-map transformation based approach
outperforms state-of-the-art continual GANs methods, with substantially fewer
parameters, and also generates high-quality samples that can be used in
generative replay based continual learning of discriminative tasks.
