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Addressing Catastrophic Forgetting in Continual Learning using Multihead Approach and Latent Alignment via Energy-Based Model

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This research aims to mitigate catastrophic forgetting in continual learning models using a novel approach.
Implemented Equilibrium Training with Prototypical Feedback for feature enhancement.
Developed a multi-head architecture for task-specific classification and inter-task separation.
Created an Energy-based Latent aligner to reduce representational drift.
Achieved 27.5% reduction in forgetting on medical datasets.
Improved accuracy by up to 21.4% on CIFAR-10 under memory constraints.
Noted an 82% increase in computational overhead, impacting performance in single-class scenarios.

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Randomized trial demonstrates improved performance in continual learning, indicating possible solutions for forgetting issues.

Thi et al. (2026) studied this question.

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