https://github.com/imirzadeh/stable-continual-learning

Towards increasing stability of neural networks for continual learning: https://arxiv.org/abs/2006.06958.pdf (NeurIPS'20)
https://github.com/imirzadeh/stable-continual-learning

catastrophic-forgetting continual-learning deep-learning lifelong-learning pytorch

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Towards increasing stability of neural networks for continual learning: https://arxiv.org/abs/2006.06958.pdf (NeurIPS'20)

• Host: GitHub
• URL: https://github.com/imirzadeh/stable-continual-learning
• Owner: imirzadeh
• License: mit
• Archived: true
• Created: 2020-04-20T03:28:25.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-03-25T00:01:57.000Z (over 1 year ago)
• Last Synced: 2024-06-25T20:53:16.272Z (5 months ago)
• Topics: catastrophic-forgetting, continual-learning, deep-learning, lifelong-learning, pytorch
• Language: Python
• Homepage:
• Size: 516 KB
• Stars: 74
• Watchers: 5
• Forks: 11
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Understanding the Role of Training Regimes in Continual Learning

Towards increasing stability of neural networks for continual learning (NeurIPS'20)

**Note: I will add an updated version of the code soon. If you have problem reproducing the results, please see the instructions for reproducing [experiment 1](https://github.com/imirzadeh/stable-continual-learning/issues/1) and [experiment 2](https://github.com/imirzadeh/stable-continual-learning/issues/5).**

## 1. Code Structure

The high level structure of the code is as follows:

```

root

├── stable_sgd

│   

└── external_libs

    └── continual_learning_algorithms

    └── hessian_eigenthings

```

1. `stable_sgd`   : implementations of our stable and plastic training regimen for SGD (in Pytorch).      

2. `external_libs`: third-party implementations we used for our experiments such as:   

    2.1 `continual_learning_algorithms` Open source implementations for A-GEM, ER-Reservoir, and EWC (in Tensorflow).   

    2.2 `hessian_eigenthings`: Open source implementation of deflated power iteration for eigenspectrum calculations (in Pytorch).  

## 2. Setup & Installation

The code is tested on Python 3.6+, PyTorch 1.5.0, and Tensorflow 1.15.2. In addition, there are some other numerical and visualization libraries that are included in ``requirements.txt`` file. However, for convenience, we provide a script for setup:   

```

bash setup_and_install.sh

```

## 3. Replicating the Results

Note: I will add an updated version of the code soon. If you have problem reproducing the results, please see the instructions for reproducing [experiment 1](https://github.com/imirzadeh/stable-continual-learning/issues/1) and [experiment 2](https://github.com/imirzadeh/stable-continual-learning/issues/5).

We provide scripts to replicate the results:   

 * 3.1 Run ```bash replicate_experiment_1.sh``` for experiment 1 (stable vs plastic).   

 * 3.2 Run ```bash replicate_experiment_2.sh``` for experiment 2 (Comparison with other methods with 20 tasks).

 * 3.3 Run ```bash replicate_appendix_c5.sh```  for the experiment in appendix C5 (Stabilizing other methods).

 

For faster replication, here we have only 3 runs per method per experiment, but we used 5 runs for the reported results.