Learning to Prompt for Continual Learning (L2P) Official Jax Implementation

L2P is a novel continual learning technique which learns to dynamically prompt a pre-trained model to learn tasks sequentially under different task transitions. Different from mainstream rehearsal-based or architecture-based methods, L2P requires neither a rehearsal buffer nor test-time task identity. L2P can be generalized to various continual learning settings including the most challenging and realistic task-agnostic setting. L2P consistently outperforms prior state-of-the-art methods. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a rehearsal buffer.

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Enviroment setup

pip install -r requirements.txt

Getting pretrained ViT model

ViT-B/16 model used in this paper can be downloaded at here.

Instructions on running L2P

We provide the configuration file to train and evaluate L2P on multiple benchmarks in configs.

To run our method on the Split CIFAR-100 dataset (class-incremental setting):

python -m main.py --my_config configs/cifar100_l2p.py --workdir=./cifar100_l2p --my_config.init_checkpoint=<ViT-saved-path/ViT-B_16.npz>

To run our method on the more complex Gaussian Scheduled CIFAR-100 dataset (task-agnostic setting):

python -m main.py --my_config configs/cifar100_gaussian_l2p.py --workdir=./cifar100_gaussian_l2p --my_config.init_checkpoint=<ViT-saved-path/ViT-B_16.npz>

Note: we run our experiments using 8 V100 GPUs or 4 TPUs, and we specify a per device batch size of 16 in the config files. This indicates that we use a total batch size of 128.

Visualize results

We use tensorboard to visualize the result. For example, if the working directory specified to run L2P is workdir=./cifar100_l2p, the command to check result is as follows:

tensorboard --logdir ./cifar100_l2p

Here are the important metrics to keep track of, and their corresponding meanings:

Cite

@inproceedings{wang2021learning,
  title={Learning to Prompt for Continual Learning},
  author={Zifeng Wang and Zizhao Zhang and Chen-Yu Lee and Han Zhang and Ruoxi Sun and Xiaoqi Ren and Guolong Su and Vincent Perot and Jennifer Dy and Tomas Pfister},
  booktitle={arXiv preprint arXiv:2112.08654},
  year={2021}
}