Continual Zero Shot Learning

Continual zero-shot learning (CZSL) aims to enable AI systems to learn new object categories without access to labeled examples and without forgetting previously learned categories, mirroring human lifelong learning. Current research focuses on developing robust model architectures that address the "catastrophic forgetting" problem and effectively leverage semantic information, often employing generative models or domain-invariant networks to improve generalization across diverse data distributions. This field is crucial for building more adaptable and robust AI systems, with potential applications ranging from species identification in ecological monitoring to improved object recognition in dynamic environments.