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This paper presents a novel sparsity-driven continual learning framework that operates in two stages: it first identifies a sparse, task-critical parameter mask via a sparsity-inducing objective, then performs mask-constrained fine-tuning by only optimizing parameters selected by the mask.
FARM-FER is proposed, which treats local and global frequency descriptors as a control signal rather than an additional classifier input, supporting a lightweight yet effective design in terms of model size and arithmetic cost for noisy-label FER.
FACET proposes an efficient replay-free task-conditioned feature consistency loss, aiming to mitigate catastrophic forgetting of the learned mixture distribution in the adapter's feature space, and demonstrates robust scalability.
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