🧠 Continual Learning System

A robust framework for training neural networks that can learn sequentially without forgetting

Overview • Key Features • Techniques • Installation • Usage • Results • Contributing

🔄 Overview

The Continual Learning System is a comprehensive framework for developing neural networks that can learn tasks sequentially without suffering from catastrophic forgetting. This project implements several state-of-the-art techniques to mitigate forgetting in neural networks, allowing them to adapt to new tasks while retaining performance on previously learned ones.

🌟 Key Features

• Task Sequential Learning: Train models on a sequence of tasks without complete retraining
• Forgetting Mitigation: Advanced techniques to prevent catastrophic forgetting
• Performance Tracking: Comprehensive metrics to monitor how well knowledge is retained
• Experiment Framework: Easily run and compare different continual learning approaches
• Visualization Tools: Track and visualize forgetting metrics across sequential tasks

🧩 Techniques Implemented

Elastic Weight Consolidation (EWC)

EWC measures the importance of neural network weights for previously learned tasks and penalizes changes to important weights when learning new tasks.

# Loss calculation with EWC
loss = task_loss + lambda_ewc * ewc_loss

Experience Replay

This technique maintains a memory buffer of examples from previous tasks and periodically replays them during training on new tasks.

# Replay during training
combined_loss = current_task_loss + alpha * replay_loss

Learning without Forgetting (LwF)

LwF uses knowledge distillation to preserve the model's behavior on previous tasks when learning new ones.

# LwF distillation loss
distillation_loss = KL_divergence(current_outputs, previous_outputs)

Task-specific Components

For some approaches, we isolate or add task-specific parameters while sharing a common feature extraction backbone.

🔧 Installation

# Clone the repository
git clone https://github.com/1Utkarsh1/continual-learning.git
cd continual-learning

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

📊 Usage

Quick Start

# Run baseline experiment (sequential training without any continual learning techniques)
python src/main.py --method baseline --tasks mnist_split

# Run EWC experiment
python src/main.py --method ewc --tasks mnist_split --lambda_ewc 5000

# Run Experience Replay experiment
python src/main.py --method replay --tasks mnist_split --buffer_size 500

Custom Task Sequences

You can define your own task sequences in a YAML configuration file:

# config/tasks/custom_sequence.yaml
task_sequence:
  - name: "mnist_digits_0_4"
    dataset: "mnist"
    classes: [0, 1, 2, 3, 4]
  
  - name: "mnist_digits_5_9"
    dataset: "mnist"
    classes: [5, 6, 7, 8, 9]
  
  - name: "fashion_mnist"
    dataset: "fashion_mnist"
    classes: "all"

📈 Experimental Results

Comparison of Methods

Method	Average Accuracy	Average Forgetting	Training Time
Naïve Fine-tuning	45.2%	35.8%	1.0x
EWC	78.5%	10.2%	1.2x
Experience Replay	82.3%	7.5%	1.5x
LwF	75.7%	12.8%	1.3x

📝 Recent Experiment Results

The following experiment results were obtained on March 11, 2025 using the MNIST split task sequence:

Baseline (Naïve Fine-tuning):

• Command: python src/main.py --method baseline --tasks mnist_split --epochs 5
• Task sequence: ['mnist_0_4', 'mnist_5_9']
• Average final accuracy: 49.74%
• Average forgetting: 49.90%

Learning without Forgetting (LwF):

• Command: python src/main.py --method lwf --tasks mnist_split --epochs 5
• Task sequence: ['mnist_0_4', 'mnist_5_9']
• Average final accuracy: 49.67%
• Average forgetting: 49.83%

🛠️ Project Structure

continual_learning/
├── src/                    # Source code
│   ├── models/             # Neural network architectures
│   ├── data/               # Data loading and preprocessing
│   ├── methods/            # Continual learning algorithms
│   ├── utils/              # Utility functions
│   └── main.py             # Main entry point
├── experiments/            # Jupyter notebooks for experiments
├── config/                 # Configuration files
│   ├── models/             # Model configurations
│   └── tasks/              # Task sequence definitions
├── results/                # Saved results and visualizations
└── docs/                   # Documentation

📝 Example Experiments

1. Split MNIST

   • Train on digits 0-4, then 5-9
   • Compare different methods' ability to remember the first task

2. Task Incremental Learning

   • Train on MNIST → Fashion-MNIST → KMNIST
   • Measure accuracy on all previous datasets after each task

3. Class Incremental Learning

   • Add new classes (one at a time) to a classifier
   • Test identification of all classes after each addition

🔮 Roadmap

• Implement baseline sequential training
• Implement Elastic Weight Consolidation (EWC)
• Implement Experience Replay
• Implement Learning without Forgetting (LwF)
• Add support for generative replay
• Implement parameter isolation methods
• Add support for continual reinforcement learning
• Develop benchmark suite for comparing methods

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📚 References

1. Kirkpatrick, J. et al. "Overcoming catastrophic forgetting in neural networks" - Proceedings of the National Academy of Sciences (2017)
2. Rebuffi, S. et al. "iCaRL: Incremental Classifier and Representation Learning" - CVPR (2017)
3. Li, Z. and Hoiem, D. "Learning without Forgetting" - IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)
4. Chaudhry, A. et al. "Efficient Lifelong Learning with A-GEM" - ICLR (2019)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by the Continual Learning Team
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