NPBCL

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Bayesian Structure Adaptation for Continual Learning. A non-parametric Bayesian approach on continual learning that learns the sparse deep substructure for each task by selecting weights to be used by the deep neural network.

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# Bayesian Structure Adaptation for Continual Learning
This repository is PyTorch implementation of the [Bayesian Structure Adaptation for Continual Learning](https://proceedings.mlr.press/v139/kumar21a.html).

If you use this repository please cite the linked paper.

## Requirements
  * Keras 2.2.5
  * PyTorch 1.3.1
  * torchvision 0.4.2
  * scipy 1.3.1
  * sklearn 0.21.3
  * numpy 1.17.2
  * matplotlib 3.1.1
  * gzip, pickle, tarfile, urllib, PIL, math, copy
  * A cuda enabled 12 GB GPU (GeForce GTX 1080 Ti) / Equivalent Devices / Google Colab

## Running the experiments
To run all experiments together (might take a while) use : `sh run_all.sh`

Individual experiments can be run using `pyhton3 experiment_name.py`
- Experiment names starts with : `npbcl_xxx.py`
- Copy and save the stored experiment models using : `echo -e "saves\ncache/destination" | python3 save.py`
- Copy and save the generative experiment images : `echo -e "Gens\ncache/destination" | python3 save.py`

## Running on Google Colaboratory
- Create a new notebook on colab and clone this repo : `! git clone https://github.com/scakc/NPBCL.git`
- Change working directory to icml20 folder : `os.chdir('NPBCL')`
- Run all experiments : `sh run_all.sh`

## Results
If you ran all experiments using sh file. You can see all experiment results in cache folder.
After individual experiment the results are stored in saves folder and Gens folder.

>The Base Model which we built upon is Varaitional Continual Learning : [here](https://github.com/nvcuong/variational-continual-learning)

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