Continual Learning
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🧠
Human-like Learning
Mimics human ability to learn new skills without forgetting old ones
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Real-time Adaptation
Continuously adapts to new patterns while serving production traffic
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Knowledge Preservation
Protects critical business knowledge from catastrophic forgetting
🧠Continual Learning Fundamentals
Core concepts and challenges in lifelong machine learning
Key Challenges
Catastrophic Forgetting
high impactLoss of previously learned knowledge when learning new tasks
Frequency: very common
Solutions: EWC, Memory Replay, Progressive Networks
Task Interference
medium impactNegative interference between conflicting tasks
Frequency: common
Solutions: Task-specific modules, Gradient episodic memory
Limited Memory
medium impactConstraints on storing examples from previous tasks
Frequency: common
Solutions: Smart sampling, Compressed representations
Concept Drift
high impactGradual changes in data distribution over time
Frequency: very common
Solutions: Adaptive learning rates, Drift detection
Learning Scenarios
Task-Incremental Learning
Learning a sequence of different tasks
Example: Image classification → Object detection → Segmentation
Domain-Incremental Learning
Same task across different domains
Example: Sentiment analysis for different product categories
Class-Incremental Learning
Adding new classes to existing classification
Example: Adding new product categories to recommendation system
Success Criteria
Backward Transfer> 85% retention
Forward Transfer> 1.2x speedup
Memory Efficiency< 2x baseline
Learning Speed< 10% slowdown
Continual Learning Taxonomy
Mathematical Framework
# Continual Learning Objective
# Goal: Learn tasks T₁, T₂, ..., Tₖ sequentially
# Traditional ML: Minimize loss on current task only
L_traditional = E[L(f_θ(x), y)] for current task
# Continual Learning: Balance current and previous tasks
L_continual = L_current(θ) + λ * L_retention(θ)
where:
- L_current: Loss on current task
- L_retention: Regularization to prevent forgetting
- λ: Balance hyperparameter
# Knowledge Retention Constraint
# Ensure: |f_θ_new(x_old) - f_θ_old(x_old)| < ε
# Forward Transfer Metric
FWT = (1/T) * Σ(R_i,i - R_i,<i)
# Backward Transfer Metric
BWT = (1/T-1) * Σ(R_T,i - R_i,i)🔗Further Learning
Related Topics
Tools & Technologies
AvalancheContinuumPyTorchTensorFlowRedisKubernetesMLflowWeights & Biases
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