Continual Learning in Real-Life Applications

Existing Continual Learning benchmarks only partially address the complexity of real-life applications, limiting the realism of learning agents. In this letter, we propose and focus on benchmarks characterized by common key elements of real-life scenarios, including temporally ordered streams as input data, strong correlation of samples in short time ranges, high data distribution drift over the long time frame, and heavy class unbalancing. Moreover, we enforce online training constraints such as the need for frequent model updates without the possibility of storing a large amount of past data or passing the dataset multiple times through the model. Besides, we introduce a novel hybrid approach based on Continual Learning, whose architectural elements and replay memory management proved to be useful and effective in the considered scenarios. The experimental validation carried out, including comparisons with existing methods and an ablation study, confirms the validity and the suitability of the proposed approach.