AR1* with Latent Replay

Introduction

In this repository you will find a pytorch re-implementation of AR1* with Latent Replay. AR1* was shown to be very effective and efficient for continual learning with real-world images.

Please consider citing the following paper if you want to use our algorithm in your research project or application:

@article{pellegrini2019,
   title = {Latent Replay for Real-Time Continual Learning},
   author = {Lorenzo Pellegrini and Gabriele Graffieti and Vincenzo Lomonaco
    and Davide Maltoni,
   journal = {Arxiv preprint arXiv:1912.01100},
   url = "https://arxiv.org/abs/1912.01100",
   year = {2019}
}

The original Caffe implementation can be found here. For more details about other variations or past versions of AR1 you can refer to these papers:

@InProceedings{lomonaco2019nicv2,
   title = {Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches},
   author = {Vincenzo Lomonaco and Davide Maltoni and Lorenzo Pellegrini},
   journal = {1st Workshop on Continual Learning in Computer Vision
    at CVPR2020},
   url = "https://arxiv.org/abs/1907.03799",
   year = {2019}
}

@article{MALTONI201956,
    title = "Continuous learning in single-incremental-task scenarios",
    journal = "Neural Networks",
    volume = "116",
    pages = "56 - 73",
    year = "2019",
    issn = "0893-6080",
    doi = "https://doi.org/10.1016/j.neunet.2019.03.010",
    url = "http://www.sciencedirect.com/science/article/pii/S0893608019300838",
    author = "Davide Maltoni and Vincenzo Lomonaco"
}

Project Structure

The project is structured as follows:

• models/: In this folder the main MobileNetV1 model is defined along with the custom Batch Renormalization Pytorch layer.
• ar1star_lat_replay.py: Main AR1* with Latent Replay algorithm.
• data_loader.py: CORe50 data loader.
• LICENSE: CC BY 4.0 Licence file.
• params.cfg: Hyperparameters that will be used in the main experiment on CORe50 NICv2-391.
• README.md: This instructions file.
• utils.py: Utility functions used in the rest of the code.

Getting Started

When using anaconda virtual environment all you need to do is run the following command and conda will install everything for you. See environment.yml:

conda env create --file environment.yml
conda activate ar1-env

Then to reproduce the results on the CORe50 NICv2-391 benchmark you just need to run the following code:

python ar1star_lat_replay.py

The results will be logged on tensorboard, you can run it with:

tensorboard --logdir logs

Then open your browser at http://localhost:6006. If everything is setup you should reach ~77% of accuracy at the end of the entire training procedure (~24m on a single TitanX GPU).

This results is a few percentage point better than the one suggested in the original paper. Keep in mind that this implementation is slightly different from the original one in Caffe for a number of reasons:

• the ImageNet pre-trained model is different.
• the pytorch SGD optimizer is different.
• the Batch Renormalization Layers are different.
• we did not find any advantage in keeping the BRN layers below the latent reply layer free to learn, so we freeze them from the first batch.

Use AR1* in Your Project

You are free to take this code and use it in your own project! However, take in mind that the hyper-parameters used in the experiment have been chosen to replicate the results shown in the paper for the CORe50 NICv2-391 scenario and may result suboptimal in different settings.

We suggest to take a look at the papers linked above to have a better idea on how to parametrize AR1* on different benchmarks. In particular we underline the importance of BN / BRN parameters, which may be fundamental to tune appropriately.

We are working to release AR1* hyper-parameter settings for other common Continual Learning benchmarks. Send an email to [email protected] in case you're interested!