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Continual-Learning-Benchmark

Evaluate three types of task shifting with popular continual learning algorithms.

This repository implemented and modularized following algorithms with PyTorch:

• EWC: code, paper (Overcoming catastrophic forgetting in neural networks)
• Online EWC: code, paper
• SI: code, paper (Continual Learning Through Synaptic Intelligence)
• MAS: code, paper (Memory Aware Synapses: Learning what (not) to forget)
• GEM: code, paper (Gradient Episodic Memory for Continual Learning)
• (More are coming)

All the above algorithms are compared to following baselines with the same static memory overhead:

• Naive rehearsal: code
• L2: code, paper

Key tables:

<img src="fig/task_shifts.png" width="640"></a> <img src="fig/results_split_mnist.png" width="640"></a>

If this repository helps your work, please cite:

@inproceedings{Hsu18_EvalCL,
  title={Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines},
  author={Yen-Chang Hsu and Yen-Cheng Liu and Anita Ramasamy and Zsolt Kira},
  booktitle={NeurIPS Continual learning Workshop },
  year={2018},
  url={https://arxiv.org/abs/1810.12488}
}

Preparation

This repository was tested with Python 3.6 and PyTorch 1.0.1.post2. Part of the cases is tested with PyTorch 1.5.1 and gives the same results.

pip install -r requirements.txt

Demo

The scripts for reproducing the results of this paper are under the scripts folder.

• Example: Run all algorithms in the incremental domain scenario with split MNIST.

./scripts/split_MNIST_incremental_domain.sh 0
# The last number is gpuid
# Outputs will be saved in ./outputs

• Eaxmple outputs: Summary of repeats

===Summary of experiment repeats: 3 / 3 ===
The regularization coefficient: 400.0
The last avg acc of all repeats: [90.517 90.648 91.069]
mean: 90.74466666666666 std: 0.23549144829955856

• Eaxmple outputs: The grid search for regularization coefficient

reg_coef: 0.1 mean: 76.08566666666667 std: 1.097717733400629
reg_coef: 1.0 mean: 77.59100000000001 std: 2.100847606721314
reg_coef: 10.0 mean: 84.33933333333334 std: 0.3592671553160509
reg_coef: 100.0 mean: 90.83800000000001 std: 0.6913701372395712
reg_coef: 1000.0 mean: 87.48566666666666 std: 0.5440161353816179
reg_coef: 5000.0 mean: 68.99133333333333 std: 1.6824762174313899

Usage

• Enable the grid search for the regularization coefficient: Use the option with a list of values, ex: -reg_coef 0.1 1 10 100 ...
• Repeat the experiment N times: Use the option -repeat N

Lookup available options:

python iBatchLearn.py -h

Other results

Below are CIFAR100 results. Please refer to the scripts for details.

<img src="fig/results_split_cifar100.png" width="640"></a>