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Condensed Composite Memory Continual Learning

This repository contains all the code used for the creation of the paper "Condensed Composite Memory Continual Learning" published at IJCNN 2021 (https://ieeexplore.ieee.org/document/9533491).

Requirements

In order to install all required packages automatically run pip install -r requirements.txt.

Running the code

Each experiment for a certain method and dataset can be run using the corresponding python script. Results will be saved in the logs folder.

Experience Replay

  • MNIST dataset: python run_experience_replay_MNIST.py
  • FashionMNIST dataset: python run_experience_replay_FashionMNIST.py
  • SVHN dataset: python run_experience_replay_SVHN.py
  • CIFAR10 dataset: python run_experience_replay_CIFAR10.py

BiC

  • MNIST dataset: python run_BiC_MNIST.py
  • FashionMNIST dataset: python run_BiC_FashionMNIST.py
  • SVHN dataset: python run_BiC_SVHN.py
  • CIFAR10 dataset: python run_BiC_CIFAR10.py

Dataset Condensation

  • MNIST dataset: python run_compressed_buffer_MNIST.py
  • FashionMNIST dataset: python run_compressed_buffer_FashionMNIST.py
  • SVHN dataset: python run_compressed_buffer_SVHN.py
  • CIFAR10 dataset: python run_compressed_buffer_CIFAR10.py

Condensed Composite Memory Continual Learning

  • MNIST dataset: python run_compositional_buffer_MNIST.py
  • FashionMNIST dataset: python run_compositional_buffer_FashionMNIST.py
  • SVHN dataset: python run_compositional_buffer_SVHN.py
  • CIFAR10 dataset: python run_compositional_buffer_CIFAR10.py

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