Understanding Imbalanced Forgetting in Rehearsal-Based Class-Incremental Learning
Rehearsal-based class-incremental learning, a standard approach to mitigating catastrophic forgetting in neural networks, exhibits a critical blind spot: certain classes degrade far more than others despite balanced memory replay. This paper isolates the gradient-level mechanisms driving asymmetric forgetting through three interpretable coefficients, revealing a systematic failure mode that existing mitigation strategies overlook. The finding matters for practitioners deploying continual learning systems in production, where uneven class performance can silently degrade model reliability without triggering obvious alarms.
Modelwire context
ExplainerThe paper's core contribution isn't that rehearsal-based learning fails, but that it fails predictably and unevenly. The three interpretable gradient coefficients provide a diagnostic tool, not just a problem statement, which means practitioners can now measure which classes are at risk before deployment.
This connects directly to the broader tension surfaced in recent work on test-time adaptation and domain-agnostic transfer. The GFMate paper from this week tackles generalization bottlenecks by decoupling tuning from source-domain bias; this imbalanced forgetting work identifies a similar hidden dependency where replay buffer composition creates systematic class-level bias. Both papers expose how standard mitigation strategies can mask failure modes that only emerge under specific data distributions. The difference: GFMate addresses it at inference time, while this work flags it as a training-time design flaw that no amount of post-hoc tuning fully corrects.
If practitioners applying the three-coefficient diagnostic to production continual learning systems report that predicted high-forgetting classes actually do degrade first in holdout evaluation, the framework has predictive value. If the coefficients fail to correlate with real-world class performance degradation on non-benchmark datasets, the work remains academically interesting but operationally limited.
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Mentionsclass-incremental learning · catastrophic forgetting · rehearsal-based learning · neural networks
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