Q&A 48.5: ContinualLearningOrchestrator — Pipeline Design & Strategy Questions

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Questions & Answers: ContinualLearningOrchestrator

Ask questions about the unified continual learning pipeline, including:

• Strategy Selection: How to automatically choose between EWC, replay, and progressive nets?
• Hybrid Approaches: Best combinations of strategies? Interference between methods?
• Benchmarking: Standard benchmarks, evaluation protocols, metric computation
• Scaling: Handling 50+ sequential tasks, growing accuracy matrix
• Stability-Plasticity: How to detect and correct imbalance?
• Real-World Deployment: Differences between academic benchmarks and production CL?
• Checkpointing: Efficient state saving for long-running CL systems
• Resource Management: Memory/compute budgeting across strategies

Frequently Asked Questions

Q: Which strategy works best in practice?
A: There is no single best method — it depends on the scenario. For Class-IL (hardest), replay-based methods (especially DER++) consistently outperform regularization-only approaches. For Task-IL with known identity, architecture-based methods provide guaranteed zero forgetting. Hybrids (EWC + Replay) often give the best overall performance.

Q: How to evaluate continual learning fairly?
A: Report the full accuracy matrix R[i][j], plus derived metrics (average accuracy, BWT, FWT, forgetting). Use established benchmarks (Split-MNIST, Split-CIFAR, CORe50) and the three scenarios framework of van de Ven & Tolias (2019). Always compare against naive fine-tuning (lower bound) and joint training (upper bound).

Q: How does this integrate with the rest of ASI-Build?
A: The orchestrator builds on Phase 47's symbolic reasoning for knowledge retention through logical constraints, Phase 46's self-supervised learning for stable representations, Phase 45's model compression for efficient multi-task storage, and Phase 44's graph neural networks for modeling task relationships.

Related: Issue #942 | S&T: see companion discussion | Planning: #937