Two brains are better than one: SAFE's dual-learner approach prevents AI amnesia
SAFE, proposed in this paper, combines a wise old turtle and quick young rabbit to help AI remember everything it learns
This paper introduces SAFE (Slow And Fast Parameter-Efficient tuning), a framework that solves catastrophic forgetting in continual learning by combining two complementary learners. The slow learner preserves general knowledge from pre-trained models, while the fast learner rapidly adapts to new concepts, achieving superior performance without storing historical data.
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https://arxiv.org/abs/2411.02175
🤔 Original Problem:
Current continual learning methods using pre-trained models either lose inherent knowledge during adaptation or lack plasticity for new concepts. They either freeze parameters completely or require storing old data, leading to suboptimal performance.
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🔧 Solution in this Paper:
→ SAFE employs a slow learner that explicitly transfers knowledge from pre-trained models using correlation matrices and transfer loss functions
→ The fast learner continuously updates to learn new concepts while being guided by the slow learner through feature alignment
→ An entropy-based aggregation strategy dynamically combines predictions from both learners during inference
→ Cross-classification loss with feature alignment prevents catastrophic forgetting without storing exemplars
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💡 Key Insights:
→ Direct parameter-efficient tuning loses general knowledge from pre-trained models
→ Freezing parameters hinders model plasticity for new concepts
→ Dynamic combination of slow and fast learners provides optimal balance
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📊 Results:
→ Surpassed state-of-the-art by 4.4% on ImageNet-A
→ Improved average accuracy by 2.1% across six datasets
→ Achieved 67.82% accuracy on DomainNet domain-incremental learning
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