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https://github.com/GT-RIPL/Continual-Learning-Benchmark
Evaluate three types of task shifting with popular continual learning algorithms.
https://github.com/GT-RIPL/Continual-Learning-Benchmark
artificial-neural-networks continual-learning continuous-learning deep-learning incremental-learning lifelong-learning
Last synced: about 1 month ago
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Evaluate three types of task shifting with popular continual learning algorithms.
- Host: GitHub
- URL: https://github.com/GT-RIPL/Continual-Learning-Benchmark
- Owner: GT-RIPL
- License: mit
- Created: 2018-11-30T16:40:23.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-04-26T10:03:40.000Z (over 3 years ago)
- Last Synced: 2024-08-03T23:24:31.687Z (5 months ago)
- Topics: artificial-neural-networks, continual-learning, continuous-learning, deep-learning, incremental-learning, lifelong-learning
- Language: Python
- Homepage:
- Size: 470 KB
- Stars: 503
- Watchers: 16
- Forks: 92
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-machine-learning-resources - **[Code Collection - RIPL/Continual-Learning-Benchmark?style=social) (Table of Contents)
README
# 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](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/regularization.py), [paper](https://arxiv.org/abs/1612.00796) (Overcoming catastrophic forgetting in neural networks)
- Online EWC: [code](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/regularization.py), [paper](https://arxiv.org/abs/1805.06370)
- SI: [code](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/regularization.py), [paper](https://arxiv.org/abs/1703.04200) (Continual Learning Through Synaptic Intelligence)
- MAS: [code](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/regularization.py), [paper](https://eccv2018.org/openaccess/content_ECCV_2018/papers/Rahaf_Aljundi_Memory_Aware_Synapses_ECCV_2018_paper.pdf) (Memory Aware Synapses: Learning what (not) to forget)
- GEM: [code](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/exp_replay.py), [paper](https://arxiv.org/abs/1706.08840) (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](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/exp_replay.py)
- L2: [code](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/agents/regularization.py), [paper](https://arxiv.org/abs/1612.00796)
Key tables:
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.
```bash
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.
```bash
./scripts/split_MNIST_incremental_domain.sh 0
# The last number is gpuid
# Outputs will be saved in ./outputs
```
- Eaxmple outputs: Summary of repeats
```text
===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
```text
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:
```bash
python iBatchLearn.py -h
```
## Other results
Below are CIFAR100 results. Please refer to the [scripts](https://github.com/GT-RIPL/Continual-Learning-Benchmark/blob/master/scripts/split_CIFAR100_incremental_class.sh) for details.
