Design Patterns & Techniques

🔗

Prompt Chaining

🔀

Routing

⚡

Parallelization

🪞

Reflection

🔧

Tool Use

🎯

Planning

👥

Multi-Agent

🧠

Memory Management

📈

Learning and Adaptation

🏗️

Fault Tolerance Infrastructure

📚

Knowledge Retrieval (RAG)

🧠

Reasoning Techniques

🔐

Security & Privacy Patterns

📊

Evaluation and Monitoring

🧠

Context Management

🎨

UI/UX & Human-AI Interaction

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🔄

Continual Learning(CL)

Sequential learning of multiple tasks while preventing catastrophic forgetting of previous knowledge

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Learn new tasks sequentially while retaining knowledge from previous tasks without catastrophic forgetting

Why: Enables lifelong learning, adapts to changing environments, and maintains accumulated knowledge over time

Key Insight: Balance plasticity for new learning with stability for old knowledge through specialized retention mechanisms

⚡ Quick Implementation

1Baseline:Train initial model on first task/domain

2Strategy:Choose catastrophic forgetting mitigation approach

3Sequential:Learn new tasks while preserving old knowledge

4Evaluate:Test performance on all tasks seen so far

5Adapt:Adjust strategy based on forgetting metrics

Example: task_sequence → continual_strategy → updated_model → performance_retention

📋 Do's & Don'ts

✅Use regularization techniques to constrain weight changes

✅Implement experience replay with diverse sample selection

✅Monitor backward and forward transfer metrics

✅Apply dynamic architectures for capacity expansion

✅Use meta-learning for rapid task adaptation

✅Implement memory-efficient storage strategies

❌Ignore catastrophic forgetting measurement

❌Use naive fine-tuning without protection mechanisms

❌Store all previous data (privacy/storage issues)

❌Skip baseline comparison with multi-task learning

❌Use single metric for continual learning evaluation

🚦 When to Use

Use When

• Sequential task arrival with limited memory
• Privacy constraints prevent storing old data
• Non-stationary environments requiring adaptation
• Lifelong learning systems deployment
• Resource-constrained edge computing scenarios

Avoid When

• All tasks available simultaneously
• Unlimited memory and computational resources
• Tasks are completely unrelated
• Static environment with fixed requirements
• Simple multi-task learning suffices

📊 Key Metrics

Average Accuracy

Mean performance across all tasks

Backward Transfer

Performance change on old tasks

Forward Transfer

Initial performance on new tasks

Catastrophic Forgetting

Performance degradation rate

Learning Efficiency

Samples needed per task

Memory Footprint

Storage requirement growth rate

💡 Top Use Cases

Conversational AI: Continuously learn new dialogue patterns and domains

Recommendation Systems: Adapt to changing user preferences over time

Autonomous Vehicles: Learn new driving scenarios without forgetting existing skills

Medical Diagnosis: Incrementally learn new diseases and conditions

Cybersecurity: Continuously adapt to new attack patterns and threats

Edge AI Devices: Update capabilities while maintaining existing functionality

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers

Catastrophic Forgetting in Connectionist Networks (French, 1999)

Overcoming Catastrophic Forgetting in Neural Networks (Kirkpatrick et al., 2017)

Continual Learning with Deep Generative Replay (Shin et al., 2017)

Gradient Episodic Memory for Continual Learning (Lopez-Paz & Ranzato, 2017)

Regularization-Based Approaches

Elastic Weight Consolidation (EWC) - Kirkpatrick et al., 2017

Synaptic Intelligence (Zenke et al., 2017)

Memory Aware Synapses (Aljundi et al., 2018)

Learning without Forgetting (Li & Hoiem, 2016)

Memory-Based Methods

Experience Replay for Continual Learning (Rolnick et al., 2019)

Efficient Lifelong Learning with A-GEM (Chaudhry et al., 2019)

Episodic Memory in Lifelong Language Learning (de Masson d'Autume et al., 2019)

Meta-Experience Replay for Continual Learning (Riemer et al., 2019)

Dynamic Architecture Methods

Progressive Neural Networks (Rusu et al., 2016)

PackNet: Adding Multiple Tasks to a Single Network (Mallya & Lazebnik, 2018)

Compacting, Picking and Growing (CPG) for Unforgetting Continual Learning (Hung et al., 2019)

Supermasks in Superposition (Wortsman et al., 2020)

Recent Advances (2022-2024)

Continual Learning with Foundation Models (Wang et al., 2023)

Online Continual Learning for Interactive Instruction Following Agents (Zheng et al., 2024)

Continual Learning in the Era of Large Language Models (Smith et al., 2024)

Memory-Efficient Continual Learning through Progressive Feature Alignment (Chen et al., 2024)

Benchmarks & Evaluation

CORe50: a New Dataset and Benchmark for Continuous Object Recognition (Lomonaco & Maltoni, 2017)

Continual Learning Benchmark (CLB) Framework (Díaz-Rodríguez et al., 2018)

AVALANCHE: an End-to-End Library for Continual Learning (Lomonaco et al., 2021)

Continual Learning Data Former (CLDF) - Structured Benchmarking (Ke et al., 2022)

Theoretical Analysis

Theoretical Analysis of Catastrophic Forgetting (Ramasesh et al., 2020)

Understanding Catastrophic Forgetting and Enabling Continual Learning (Ven & Tolias, 2019)

Reconciling Continual Learning and Meta-Learning (He & Jaeger, 2021)

The Role of Replay in Continual Learning (Buzzega et al., 2020)

Applications & Domains

Continual Learning for Natural Language Processing (Biesialska et al., 2020)

Lifelong Learning in Computer Vision (Parisi et al., 2019)

Continual Learning for Robotics (Lesort et al., 2020)

Continual Learning in Recommender Systems (Kang & McAuley, 2018)

Implementation Resources

AVALANCHE: End-to-End Continual Learning Library

Continual Learning Baselines (Facebook Research)

PyTorch Continual Learning Framework

Sequoia: A Software Framework for Research in Continual Learning

Tools & Libraries

ContinualAI/avalanche: Comprehensive CL library

pytorch/ignite: Continual learning utilities

Continuum: PyTorch library for continual learning

River: Online machine learning in Python

Surveys & Reviews

Continual Lifelong Learning with Neural Networks: A Review (Parisi et al., 2019)

Three scenarios for continual learning (van de Ven & Tolias, 2019)

A Comprehensive Survey of Continual Learning (Wang et al., 2023)

Continual Learning: A Comparative Study on How to Defy Forgetting (Buzzega et al., 2021)

Community & Research Groups

ContinualAI: International Research Community

CLVISION Workshop (CVPR/ICCV)

NeurIPS Continual Learning Workshop

ICML Lifelong Learning Workshop

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

🔄

Continual Learning(CL)

Sequential learning of multiple tasks while preventing catastrophic forgetting of previous knowledge

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Learn new tasks sequentially while retaining knowledge from previous tasks without catastrophic forgetting

Why: Enables lifelong learning, adapts to changing environments, and maintains accumulated knowledge over time

Key Insight: Balance plasticity for new learning with stability for old knowledge through specialized retention mechanisms

⚡ Quick Implementation

1Baseline:Train initial model on first task/domain

2Strategy:Choose catastrophic forgetting mitigation approach

3Sequential:Learn new tasks while preserving old knowledge

4Evaluate:Test performance on all tasks seen so far

5Adapt:Adjust strategy based on forgetting metrics

Example: task_sequence → continual_strategy → updated_model → performance_retention

📋 Do's & Don'ts

✅Use regularization techniques to constrain weight changes

✅Implement experience replay with diverse sample selection

✅Monitor backward and forward transfer metrics

✅Apply dynamic architectures for capacity expansion

✅Use meta-learning for rapid task adaptation

✅Implement memory-efficient storage strategies

❌Ignore catastrophic forgetting measurement

❌Use naive fine-tuning without protection mechanisms

❌Store all previous data (privacy/storage issues)

❌Skip baseline comparison with multi-task learning

❌Use single metric for continual learning evaluation

🚦 When to Use

Use When

• Sequential task arrival with limited memory
• Privacy constraints prevent storing old data
• Non-stationary environments requiring adaptation
• Lifelong learning systems deployment
• Resource-constrained edge computing scenarios

Avoid When

• All tasks available simultaneously
• Unlimited memory and computational resources
• Tasks are completely unrelated
• Static environment with fixed requirements
• Simple multi-task learning suffices

📊 Key Metrics

Average Accuracy

Mean performance across all tasks

Backward Transfer

Performance change on old tasks

Forward Transfer

Initial performance on new tasks

Catastrophic Forgetting

Performance degradation rate

Learning Efficiency

Samples needed per task

Memory Footprint

Storage requirement growth rate

💡 Top Use Cases

Conversational AI: Continuously learn new dialogue patterns and domains

Recommendation Systems: Adapt to changing user preferences over time

Autonomous Vehicles: Learn new driving scenarios without forgetting existing skills

Medical Diagnosis: Incrementally learn new diseases and conditions

Cybersecurity: Continuously adapt to new attack patterns and threats

Edge AI Devices: Update capabilities while maintaining existing functionality

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers

Catastrophic Forgetting in Connectionist Networks (French, 1999)

Overcoming Catastrophic Forgetting in Neural Networks (Kirkpatrick et al., 2017)

Continual Learning with Deep Generative Replay (Shin et al., 2017)

Gradient Episodic Memory for Continual Learning (Lopez-Paz & Ranzato, 2017)

Regularization-Based Approaches

Elastic Weight Consolidation (EWC) - Kirkpatrick et al., 2017

Synaptic Intelligence (Zenke et al., 2017)

Memory Aware Synapses (Aljundi et al., 2018)

Learning without Forgetting (Li & Hoiem, 2016)

Memory-Based Methods

Experience Replay for Continual Learning (Rolnick et al., 2019)

Efficient Lifelong Learning with A-GEM (Chaudhry et al., 2019)

Episodic Memory in Lifelong Language Learning (de Masson d'Autume et al., 2019)

Meta-Experience Replay for Continual Learning (Riemer et al., 2019)

Dynamic Architecture Methods

Progressive Neural Networks (Rusu et al., 2016)

PackNet: Adding Multiple Tasks to a Single Network (Mallya & Lazebnik, 2018)

Compacting, Picking and Growing (CPG) for Unforgetting Continual Learning (Hung et al., 2019)

Supermasks in Superposition (Wortsman et al., 2020)

Recent Advances (2022-2024)

Continual Learning with Foundation Models (Wang et al., 2023)

Online Continual Learning for Interactive Instruction Following Agents (Zheng et al., 2024)

Continual Learning in the Era of Large Language Models (Smith et al., 2024)

Memory-Efficient Continual Learning through Progressive Feature Alignment (Chen et al., 2024)

Benchmarks & Evaluation

CORe50: a New Dataset and Benchmark for Continuous Object Recognition (Lomonaco & Maltoni, 2017)

Continual Learning Benchmark (CLB) Framework (Díaz-Rodríguez et al., 2018)

AVALANCHE: an End-to-End Library for Continual Learning (Lomonaco et al., 2021)

Continual Learning Data Former (CLDF) - Structured Benchmarking (Ke et al., 2022)

Theoretical Analysis

Theoretical Analysis of Catastrophic Forgetting (Ramasesh et al., 2020)

Understanding Catastrophic Forgetting and Enabling Continual Learning (Ven & Tolias, 2019)

Reconciling Continual Learning and Meta-Learning (He & Jaeger, 2021)

The Role of Replay in Continual Learning (Buzzega et al., 2020)

Applications & Domains

Continual Learning for Natural Language Processing (Biesialska et al., 2020)

Lifelong Learning in Computer Vision (Parisi et al., 2019)

Continual Learning for Robotics (Lesort et al., 2020)

Continual Learning in Recommender Systems (Kang & McAuley, 2018)

Implementation Resources

AVALANCHE: End-to-End Continual Learning Library

Continual Learning Baselines (Facebook Research)

PyTorch Continual Learning Framework

Sequoia: A Software Framework for Research in Continual Learning

Tools & Libraries

ContinualAI/avalanche: Comprehensive CL library

pytorch/ignite: Continual learning utilities

Continuum: PyTorch library for continual learning

River: Online machine learning in Python

Surveys & Reviews

Continual Lifelong Learning with Neural Networks: A Review (Parisi et al., 2019)

Three scenarios for continual learning (van de Ven & Tolias, 2019)

A Comprehensive Survey of Continual Learning (Wang et al., 2023)

Continual Learning: A Comparative Study on How to Defy Forgetting (Buzzega et al., 2021)

Community & Research Groups

ContinualAI: International Research Community

CLVISION Workshop (CVPR/ICCV)

NeurIPS Continual Learning Workshop

ICML Lifelong Learning Workshop

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

Patterns

closed

Design Patterns & Techniques

🔗

Prompt Chaining

🔀

Routing

⚡

Parallelization

🪞

Reflection

🔧

Tool Use

🎯

Planning

👥

Multi-Agent

🧠

Memory Management

📈

Learning and Adaptation

🏗️

Fault Tolerance Infrastructure

📚

Knowledge Retrieval (RAG)

🧠

Reasoning Techniques

🔐

Security & Privacy Patterns

📊

Evaluation and Monitoring

🧠

Context Management

🎨

Agentic Design

Agentic Design

Design Patterns & Techniques

Prompt Chaining

Routing

Parallelization

Reflection

Tool Use

Planning

Multi-Agent

Memory Management

Learning and Adaptation

Reinforcement Learning from Human Feedback(RLHF)

Direct Preference Optimization(DPO)

In-Context Learning(ICL)

Meta-Learning Systems(MLS)

Continual Learning(CL)

Self-Improving Systems(SIS)

Constitutional AI(CAI)

Reinforcement Learning from AI Feedback(RLAIF)

Test-Time Scaling(TTS)

Odds Ratio Preference Optimization(ORPO)

Simple Preference Optimization(SimPO)

Supervised Learning for Agents(SLA)

Unsupervised Learning for Agents(ULA)

Online Learning for Agents(OLA)

Fault Tolerance Infrastructure

Knowledge Retrieval (RAG)

Reasoning Techniques

Security & Privacy Patterns

Evaluation and Monitoring

Context Management

UI/UX & Human-AI Interaction

Loading...

Continual Learning(CL)

🎯 30-Second Overview

⚡ Quick Implementation

📋 Do's & Don'ts

🚦 When to Use

Use When

Avoid When

📊 Key Metrics

💡 Top Use Cases

References & Further Reading

Foundational Papers

Regularization-Based Approaches

Memory-Based Methods

Dynamic Architecture Methods

Recent Advances (2022-2024)

Benchmarks & Evaluation

Theoretical Analysis

Applications & Domains

Implementation Resources

Tools & Libraries

Surveys & Reviews

Community & Research Groups

Contribute to this collection

Continual Learning(CL)

🎯 30-Second Overview

⚡ Quick Implementation

📋 Do's & Don'ts

🚦 When to Use

Use When

Avoid When

📊 Key Metrics

💡 Top Use Cases

References & Further Reading

Foundational Papers

Regularization-Based Approaches

Memory-Based Methods

Dynamic Architecture Methods

Recent Advances (2022-2024)

Benchmarks & Evaluation

Theoretical Analysis

Applications & Domains

Implementation Resources

Tools & Libraries

Surveys & Reviews

Community & Research Groups

Contribute to this collection