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AI Research
arXiv CS.AI·May 21·6 min read
MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
TL;DR
Online continual learning (OCL)—where neural networks learn from streaming data in one pass with limited memory—struggles with catastrophic forgetting: networks forget old tasks while learning new ones. MANGO (Meta-Adaptive Network Gradient Optimization) uses meta-learning to dynamically adjust how gradients update the network, balancing stability (retaining old knowledge) against plasticity (absorbing new information).
✦ Why It Matters
Engineers building systems that learn from live, non-repeating data streams can use MANGO to prevent model degradation on past tasks without expensive full retraining.
Key Takeaways
- Use meta-learning to dynamically adjust gradient updates for streaming data scenarios.
- Balance task stability and plasticity without storing full historical datasets.
- Apply MANGO framework when deploying models on non-stationary data streams.
- Evaluate continual learning systems on forward transfer and task retention metrics.
Full Summary
Online Continual Learning requires neural networks to learn sequentially from non-stationary data streams (data whose statistical properties change over time) in single passes, using only a small replay buffer for memory. Unlike offline continual learning with multiple training epochs on fixed datasets, OCL faces the stability-plasticity dilemma: maintaining performance on past tasks while efficiently acquiring new skills.
MANGO addresses this by employing meta-learning—training a system to learn how to learn—to adaptively optimize gradient updates at each step. Rather than applying fixed learning rules, MANGO's meta-adaptive approach learns which gradient modifications best preserve old knowledge while enabling new learning.
The technique was evaluated on standard OCL benchmarks, showing measurable improvements in task retention and forward transfer metrics compared to existing methods.