Model Zoo

Implementation of Model Zoo: A Growing "Brain" That Learns Continually (ICLR 22)

Model Zoo tackles a sequence of tasks and aims to leverage past tasks to better solve new tasks, and use new tasks to improve upon past tasks. Model Zoo explicitly splits the capacity of the model to mitigate task-competition and better exploit the relationship between of tasks.

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}
}