Phase 50 Planning — Meta-Learning & Learning-to-Learn Architectures

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Phase 50 — Meta-Learning & Learning-to-Learn Architectures

Overview

Phase 50 introduces meta-learning capabilities — systems that learn how to learn efficiently from limited data and rapidly adapt to new tasks. This phase implements gradient-based meta-learners (MAML, Reptile), metric-based few-shot learners (Prototypical Networks, Matching Networks), and learned optimizers that generalize across task distributions.

Motivation

Traditional deep learning assumes abundant task-specific training data and trains models from scratch or fine-tunes from pretrained weights. Meta-learning instead optimizes the learning algorithm itself, enabling:

• Few-shot generalization: classify new categories from 1–5 examples
• Rapid adaptation: converge in a handful of gradient steps on new tasks
• Task-agnostic representations: learn embeddings that transfer across task distributions
• Learned optimization: replace hand-designed optimizers with neural networks that learn update rules

Sub-phases

Sub-phase	Component	Focus
50.1	TaskDistributionSampler	Meta-task construction, episode generation, curriculum strategies
50.2	GradientMetaLearner	MAML, Reptile, first-order approximations, meta-gradient computation
50.3	MetricMetaLearner	Prototypical networks, matching networks, relation networks
50.4	MetaOptimizer	Learned optimizers, LSTM-based update rules, learning rate adaptation
50.5	MetaLearningOrchestrator	Unified pipeline, strategy selection, evaluation protocols

Architecture

┌─────────────────────────────────────────────────────┐
│              MetaLearningOrchestrator                │
│  ┌───────────────────────────────────────────────┐  │
│  │           Strategy Selection Layer             │  │
│  │  gradient-based │ metric-based │ optimization  │  │
│  └───────────┬──────────┬──────────────┬─────────┘  │
│              │          │              │             │
│  ┌───────────▼──┐ ┌────▼──────┐ ┌─────▼──────────┐ │
│  │ Gradient     │ │ Metric    │ │ Meta           │ │
│  │ MetaLearner  │ │MetaLearner│ │ Optimizer      │ │
│  │ (MAML/       │ │(Proto/    │ │(LSTM updater/  │ │
│  │  Reptile)    │ │ Matching/ │ │ meta-SGD/      │ │
│  │              │ │ Relation) │ │ learned LR)    │ │
│  └──────┬───────┘ └─────┬────┘ └──────┬─────────┘ │
│         │               │             │            │
│  ┌──────▼───────────────▼─────────────▼──────────┐ │
│  │          TaskDistributionSampler              │ │
│  │  N-way K-shot episodes │ curriculum │ replay  │ │
│  └───────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────┘

Academic References

1. Finn, C., Abbeel, P., & Levine, S. (2017). "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks." ICML 2017. — Introduced MAML, the foundational gradient-based meta-learning algorithm.

2. Nichol, A., Achiam, J., & Schulman, J. (2018). "On First-Order Meta-Learning Algorithms." arXiv:1803.02999. — Proposed Reptile, a simpler first-order alternative to MAML.

3. Snell, J., Swersky, K., & Zemel, R. (2017). "Prototypical Networks for Few-Shot Learning." NeurIPS 2017. — Metric-based meta-learning computing class prototypes in embedding space.

4. Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016). "Matching Networks for One Shot Learning." NeurIPS 2016. — Attention-based few-shot learner with episodic training.

5. Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., & Hospedales, T.M. (2018). "Learning to Compare: Relation Network for Few-Shot Learning." CVPR 2018. — Learned distance metric via relation modules.

6. Hospedales, T., Antoniou, A., Micaelli, P., & Storkey, A. (2021). "Meta-Learning in Neural Networks: A Survey." IEEE TPAMI. — Comprehensive taxonomy of meta-learning approaches.

7. Andrychowicz, M., et al. (2016). "Learning to Learn by Gradient Descent by Gradient Descent." NeurIPS 2016. — LSTM-based learned optimizers.

8. Ravi, S. & Larochelle, H. (2017). "Optimization as a Model for Few-Shot Learning." ICLR 2017. — Meta-learner LSTM for initialization and update rules.

9. Lee, K., Maji, S., Ravichandran, A., & Soatto, S. (2019). "Meta-Learning with Differentiable Convex Optimization." CVPR 2019. — MetaOptNet with convex base learners.

10. Raghu, A., Raghu, M., Bengio, S., & Vinyals, O. (2020). "Rapid Learning or Feature Reuse?" ICLR 2020. — Analysis of MAML internals.

Success Criteria

• N-way K-shot classification accuracy matching state-of-the-art on miniImageNet/tieredImageNet
• MAML adaptation in ≤ 5 inner-loop gradient steps
• Prototypical network inference with O(N·K) prototype computation
• Learned optimizer convergence improvement ≥ 20% over Adam baseline
• Full pipeline: task sampling → meta-training → evaluation in unified API

Dependencies

• Phase 48 (Continual Learning) for experience replay integration
• Phase 49 (Program Synthesis) for automated task generation
• Phase 27 (Transfer Learning) for cross-domain generalization