S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning

This is the official implementation of our NeurIPS 2022 paper "S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning". In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches. The best of the proposed approaches achieves a remarkable relative improvement (an average of about 30%) over the best of the state-of-the-art exemplar-free methods for three standard Domain Incremental Learning (DIL) tasks.

S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning
Yabin Wang, Zhiwu Huang, Xiaopeng Hong. 2022 Conference on Neural Information Processing Systems (NeurIPS 22).
[Paper]

Introduction

S-Prompts introduce a rule-breaking idea to play a win-win game for domain incremental learning. Specifically, S-Prompts uses a new prompting paradigm that learns the prompts independently domain by domain, and incrementally inserts the learned prompts in a pool. The learning process only requests the most naive cross-entropy loss for supervision. The inference is also simple and efficient. It simply uses K-NN to search for the nearest domain center generated by K-Means on the training data to the features of the given test sample, followed by prepending the learned domain-associated prompts with the image tokens to the transformer for the final classification. As S domain-related prompts will be finally learned independently, where S is the total number of domains, we name the proposed paradigm as S-Prompts or S-Prompting for simplicity throughout the paper. We hope the proposed S-Prompting becomes an Occam’s Razor (i.e., a simple and elegant principle) for DIL.

Enviroment setup

Create the virtual environment for S-Prompts.

conda env create -f environment.yaml

After this, you will get a new environment sp that can conduct S-Prompts experiments.
Run conda activate sp to activate.

Note that only NVIDIA GPUs are supported for now, and we use NVIDIA RTX 3090.

Dataset preparation

Please refer to the following links to download three standard domain incremental learning benchmark datasets.

CDDB
CORe50
DomainNet

Unzip the downloaded files, and you will get the following folders.

CDDB
├── biggan
│   ├── train
│   └── val
├── gaugan
│   ├── train
│   └── val
├── san
│   ├── train
│   └── val
├── whichfaceisreal
│   ├── train
│   └── val
├── wild
│   ├── train
│   └── val
... ...

core50
└── core50_128x128
    ├── labels.pkl
    ├── LUP.pkl
    ├── paths.pkl
    ├── s1
    ├── s2
    ├── s3
    ...

domainnet
├── clipart
│   ├── aircraft_carrier
│   ├── airplane
│   ... ...
├── clipart_test.txt
├── clipart_train.txt
├── infograph
│   ├── aircraft_carrier
│   ├── airplane
│   ... ...
├── infograph_test.txt
├── infograph_train.txt
├── painting
│   ├── aircraft_carrier
│   ├── airplane
│   ... ...
... ...

Training:

Please change the data_path in the config files to the locations of the datasets。

Currently, there are two options for net_type in the config files: slip and sip.
slip means S-liPrompts and sip means S-iPrompts.

Feel free to change the parameters in the config files, following scripts will reproduce the main results in our paper.

CDDB:

python main.py --config configs/cddb_slip.json
python main.py --config configs/cddb_sip.json

CORe50:

python main.py --config configs/core50_slip.json

DomainNet:

python main.py --config configs/domainnet_slip.json

Evaluation:

Please refer to [Evaluation Code].

Results

License

Please check the MIT license that is listed in this repository.

Acknowledgments

We thank the following repos providing helpful components/functions in our work.

Citation

If you use any content of this repo for your work, please cite the following bib entry:

@inproceedings{wang2022sprompt,
  title={S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning},
  author={Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
  year={2022}
}

G-U-N/S-Prompts