EvoCL: Continual Learning over Evolving Domains

Continual Learning aspires to build models capable of learning new tasks, without forgetting previously learnt tasks. In real-world settings, the distributions underlying the tasks are prone to shift. This necessitates a model capable of observing how the task distributions drift with time and adapt proactively. We present a novel framework of continual learning under evolving domains. Our approach employs a hypernetwork with separate embeddings conditioned on both domain and task to address this problem. The hypernetwork generates customised classifier weights corresponding to any domain-task pair. We employ a separate network that is trained end to end along with the hypernetwork to predict the next domain embedding, which in turn helps to generate classifier parameters corresponding to the next future domain in the evolution. We conduct extensive experiments on various datasets with a wide variety of distribution shifts to demonstrate the efficacy of our model in generalizing to future domains across all the tasks.