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A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
| Date | Stars |
|---|---|
| 2026-07-31 | 252 |
| 2026-08-06 | 252 |
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# Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: * Brain-inspired replay for continual learning with artificial neural networks: https://www.nature.com/articles/s41467-020-17866-2 This paper proposes a new, brain-inspired version of generative replay that can scale to continual learning problems with natural images as inputs. This is demonstrated with the Split CIFAR-100 protocol, both for task-incremental learning and for class-incremental learning. ## Installation & requirements The current version of the code has been tested with `Python 3.5.2` on several Linux operating systems with the following versions of PyTorch and Torchvision: * `pytorch 1.1.0` * `torchvision 0.2.2` The versions that were used for other Python-packages are listed in `requirements.txt`. To use the code, download the repository and change into it: ```bash git clone https://github.com/GMvandeVen/brain-inspired-replay.git cd brain-inspired-replay ``` (If downloading the zip-file, extract the files and change into the extracted folder.) Assuming Python and pip are set up, the Python-packages used by this code can be installed using: ```bash pip install -r requirements.txt ``` However, you might want to install pytorch and torchvision in a slightly different way to ensure compatability with your version of CUDA (see https://pytorch.org/). Finally, the code in this repository itself does not need to be installed, but a number of scripts should be made executable: ```bash chmod +x main_*.py compare_*.py create_figures.sh ``` ## Demos #### Demo 1: Brain-inspired replay on split MNIST ```bash ./main_cl.py --experiment=splitMNIST --scenario=class --replay=generative --brain-inspired --pdf ``` This runs a single continual learning experiment: brain-inspired replay on the class-incremental learning scenario of split MNIST. Information about the data, the model, the training progress and the produced outputs (e.g., a pdf with results) is printed to the screen. Expected run-time on a standard laptop is ~12 minutes, with a GPU it should take ~4 minutes. #### Demo 2: Comparison of continual learning methods ```bash ./compare_MNIST.py --scenario=class ``` This runs a series of continual learning experiments to compare the performance of various methods. Information about the different experiments, their progress and the produced outputs (e.g., a summary pdf) is printed to the screen. Expected run-time on a standard laptop is ~50 minutes, with a GPU it should take ~18 minutes. These two demos can also be run with on-the-fly plots using the flag `--visdom`. For this visdom must be activated first, see instructions below. ## Running comparisons from the paper The script `create_figures.sh` provides step-by-step instructions for re-running the experiments and re-creating the figures reported in the paper. Although it is possible to run this script as it is, it will take very long and it is probably sensible to parallellize the experiments. ## Running custom experiments Using `main_cl.py`, it is possible to run custom individual experiments. The main options for this script are: - `--experiment`: which task protocol? (`splitMNIST`|`permMNIST`|`CIFAR100`) - `--scenario`: according to which scenario? (`task`|`domain`|`class`) - `--tasks`: how many tasks? To run specific methods, use the following: - Context-dependent-Gating (XdG): `./main_cl.py --xdg --xdg-prop=0.8` - Elastic Weight Consolidation (EWC): `./main_cl.py --ewc --lambda=5000` - Online EWC: `./main_cl.py --ewc --online --lambda=5000 --gamma=1` - Synaptic Intelligenc (SI): `./main_cl.py --si --c=0.1` - Learning without Forgetting (LwF): `./main_cl.py --replay=current --distill` - Generative Replay (GR): `./main_cl.py --replay=generative` - Brain-Inspired Replay (BI-R): `./main_cl.py --replay=generative --brain-inspired` For information on further options: `./main_cl.py -h`. PyTorch-implementa
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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