Latent Replay

Latent replay is a continual learning technique aiming to mitigate catastrophic forgetting—the phenomenon where neural networks forget previously learned information when adapting to new data. Current research focuses on improving the efficiency and robustness of latent replay, exploring methods like generative models (e.g., VAEs, Gaussian Mixture Models) to generate synthetic data representing past experiences, rather than storing the original data itself, thereby addressing privacy concerns. This approach is being applied across various architectures, including spiking neural networks and binary neural networks, to enable continual learning in resource-constrained environments, with applications in medical image analysis and other domains requiring continuous adaptation. The ultimate goal is to develop more efficient and privacy-preserving continual learning methods for real-world applications.