Phase 48 Planning: Continual Learning & Catastrophic Forgetting Prevention

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Phase 48 — Continual Learning & Catastrophic Forgetting Prevention

Following Phase 47 (Symbolic AI & Neurosymbolic Integration), Phase 48 tackles one of the most critical challenges in deployed AI systems: how to learn new tasks and domains without forgetting previously acquired knowledge.

Motivation

Neural networks suffer from catastrophic forgetting — when trained on new data, they rapidly lose performance on earlier tasks. This is a fundamental barrier to building truly autonomous, lifelong learning systems. ASI-Build Phase 48 implements state-of-the-art continual learning strategies spanning regularization-based, architecture-based, and replay-based approaches.

Sub-Phase Roadmap

Sub-Phase	Component	Focus
48.1	ElasticWeightConsolidator	EWC, Fisher information, online EWC, parameter importance, SI
48.2	ProgressiveNetworkExpander	Progressive nets, lateral connections, column architecture, dynamic capacity
48.3	ExperienceReplayBuffer	Episodic replay, generative replay, dark experience replay, reservoir sampling
48.4	TaskDetector	Task boundary detection, distribution shift, concept drift, curriculum awareness
48.5	ContinualLearningOrchestrator	Unified pipeline, stability-plasticity balance, multi-strategy coordination

Three Continual Learning Scenarios (van de Ven & Tolias, 2019)

1. Task-Incremental Learning (Task-IL) — Task identity provided at test time; model selects task-specific output head
2. Domain-Incremental Learning (Domain-IL) — Task identity unknown; input distribution shifts but output space remains the same
3. Class-Incremental Learning (Class-IL) — Task identity unknown; new classes added over time; hardest scenario

ASI-Build supports all three scenarios with strategy-specific optimizations.

Core Approaches

Regularization-Based (48.1)

• Elastic Weight Consolidation (EWC) — Kirkpatrick et al. (2017): Penalize changes to parameters important for previous tasks using Fisher information matrix
• Synaptic Intelligence (SI) — Zenke et al. (2017): Online importance estimation via path integral of gradient × parameter change
• Memory Aware Synapses (MAS) — Aljundi et al. (2018): Importance based on sensitivity of learned function output

Architecture-Based (48.2)

• Progressive Neural Networks — Rusu et al. (2016): Freeze old columns, add new columns with lateral connections
• PackNet — Mallya & Lazebnik (2018): Iterative pruning to free capacity for new tasks
• Dynamic Expandable Networks — Yoon et al. (2018): Selective retraining and network expansion

Replay-Based (48.3)

• Experience Replay — Store and replay exemplars from previous tasks
• Generative Replay — Shin et al. (2017): Train a generative model to produce pseudo-exemplars
• Gradient Episodic Memory (GEM) — Lopez-Paz & Ranzato (2017): Constrain gradients to not increase loss on stored exemplars
• iCaRL — Rebuffi et al. (2017): Incremental classifier with representation learning and nearest-mean-of-exemplars
• Dark Experience Replay — Buzzega et al. (2020): Store and replay logits alongside inputs

Key References

1. Kirkpatrick et al. (2017) — Overcoming catastrophic forgetting in neural networks (EWC)
2. Rusu et al. (2016) — Progressive Neural Networks
3. Zenke et al. (2017) — Continual Learning through Synaptic Intelligence
4. Lopez-Paz & Ranzato (2017) — Gradient Episodic Memory for Continual Learning
5. Aljundi et al. (2018) — Memory Aware Synapses: Learning what (not) to forget
6. Shin et al. (2017) — Continual Learning with Deep Generative Replay
7. van de Ven & Tolias (2019) — Three scenarios for continual learning
8. Mallya & Lazebnik (2018) — PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
9. Rebuffi et al. (2017) — iCaRL: Incremental Classifier and Representation Learning
10. De Lange et al. (2021) — A continual learning survey: Defying forgetting in classification tasks
11. Buzzega et al. (2020) — Dark Experience for General Continual Learning
12. Yoon et al. (2018) — Lifelong Learning with Dynamically Expandable Networks

Architecture Integration

Phase 48 builds on Phase 47's symbolic reasoning (knowledge retention through logical constraints), Phase 46's self-supervised learning (representation stability), and Phase 39's explainability (understanding what the model forgets and why).

Let's discuss the roadmap, priorities, and any technical considerations!