  Algorithms designed for addressing typical supervised classification problems
can only learn from a fixed set of samples and labels, making them unsuitable
for the real world, where data arrives as a stream of samples often associated
with multiple labels over time. This motivates the study of task-agnostic
continual multi-label learning problems. While algorithms using deep learning
approaches for continual multi-label learning have been proposed in the recent
literature, they tend to be computationally heavy. Although spiking neural
networks (SNNs) offer a computationally efficient alternative to artificial
neural networks, existing literature has not used SNNs for continual
multi-label learning. Also, accurately determining multiple labels with SNNs is
still an open research problem. This work proposes a dual output spiking
architecture (DOSA) to bridge these research gaps. A novel imbalance-aware loss
function is also proposed, improving the multi-label classification performance
of the model by making it more robust to data imbalance. A modified F1 score is
presented to evaluate the effectiveness of the proposed loss function in
handling imbalance. Experiments on several benchmark multi-label datasets show
that DOSA trained with the proposed loss function shows improved robustness to
data imbalance and obtains better continual multi-label learning performance
than CIFDM, a previous state-of-the-art algorithm.
