Model Zoo

Implementation of Model Zoo: A Growing "Brain" That Learns Continually (ICLR 22). Jump into this notebook (which Ashwin and I coded up) to get started on a simplified version of Model Zoo.

Setup:

To install a working environment run:

conda env create -f env.yaml

Download the .pkl files for Mini-imagenet (link) and copy the files to ./data/mini_imagenet/

Usage

The file modelzoo.py is used to run the Zoo. The -h flag can be used to list the argparse arguments. For example to run Model Zoo:

python modelzoo.py --data_config ./config/dataset/coarse_cifar100.yaml \
                   --hp_config ./config/hyperparam/wrn.yaml \
                   --epochs 100 --replay_frac 1.0

Directory Structure

├── modelzoo.py                   # Implementation of Model Zoo
├── config:                       # Configuration files
│   ├── dataset                    
│   └── hyperparam                  
├── datasets                      # Datasets and Dataloaders
│   ├── build_dataset.py          
│   ├── cifar.py                 
│   ├── data.py                 
│   ├── mini_imagenet.py           
│   ├── mnist.py               
│   ├── modmnist.py           
├── net                           # Neural network architectures
│   ├── build_net.py
│   └── wideresnet.py
│   └── smallconv.py
└── utils                         # Utilities for logging/training
    ├── config.py
    ├── logger.py
    └── run_net.py

If you find this code useful, consider citing

@inproceedings{
    ramesh2022model,
    title={Model Zoo: A Growing Brain That Learns Continually},
    author={Rahul Ramesh and Pratik Chaudhari},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=WfvgGBcgbE7}
}