Memory replay GANs are continual learning frameworks based on generative adversarial networks that prevent catastrophic forgetting when sequentially learning new categories or tasks. Rather than storing and accessing historical training data, which can introduce storage burdens and data privacy concerns, the architecture leverages a generative replay mechanism to reproduce synthetic samples of previously learned distributions. When new tasks arrive, the model combines newly presented real data with these replayed artificial samples through strategies such as joint training or replay alignment, enabling the generative model to preserve synthesis performance on past categories while continuously acquiring knowledge of novel classes.
