├── Common 
    ├── modular.py          # Module (QLayer) and Stacked modules (QStack). Includes most key ops, such as adaptive buffer        
    ├── quantize.py         # Discretization Ops (GumbelSoftmax, Vector/Tensor Quantization and Argmax Quantization)
    ├── model.py            # Encoder, Decoder, Classifier Blocks 
├── config                  # .yaml files specifying different AQM architectures and hyperparameters used in the paper 
├── Lidar
    ├── ....                # files to run LiDAR experiments 
├── Utils             
    ├── args.py             # Contains command-line args
    ├── buffer.py           # Basic buffer implementation. Handled raw and compressed representations
    ├── data.py             # CL datasets and dataloaders
    ├── utils.py            # Logging / Saving & Loading Models, Args, point cloud processing
    
├── gen_main.py             # files to run the offline classification (e.g. Imagenet) experiments 
├── eval.py                 # evaluation loops for drift, test acc / mse, and lidar
├── cls_main.py             # files to run the online classification (e.g. CIFAR) experiments

├── reproduce.txt           # All command and information to reproduce the results in the paper

@article{caccia2019online,
  title={Online Learned Continual Compression with Adaptive Quantization Modules},
  author={Caccia, Lucas and Belilovsky, Eugene and Caccia, Massimo and Pineau, Joelle},
  journal={Proceedings of the 37th International Conference on Machine Learning},
  year={2020}
}