Improved Imbalance Resilience in Continual Multi-Label Classification with Adaptive Margin Spiking Neural Networks

Multi-label learning and continual multi-label learning are crucial challenges in machine learning, particularly in handling complex data with multiple overlapping labels over time. Recent research works try to tackle the effect of data imbalance as it makes multi-label learning more challening. This work introduces an adaptive margin spiking neural net-work (AM-SNN) architecture coupled with a novel imbalance-sensitive loss function designed to enhance robustness against class imbalance in these settings. AM-SNN employs two output layers: one for predictions and another for margin values, with a unique loss function leveraging cosine similarity between predictions and ground truths to improve confidence in the model's predictions. Experiments show that AM-SNNs trained with the proposed loss function outperform state-of-the-art loss functions on metrics such as the imbalance-weighted F1 score and the F1 score for the most imbalanced class on several multi-label learning datasets. In continual multi-label learning, AM-SNNs surpass Bipolar SNNs and the CIFDM benchmark on large datasets - Birds, Human, and Eukaryote.