Meta Learning for Few-Shot One-class Classification

Official PyTorch implementation of "Meta Learning for Few-Shot One-class Classification" by Gabriel Dahia, Maurício Pamplona Segundo, 2020.

Installation

The code is tested in Ubuntu 16.04 with Python 3.6.9. To install the dependencies, we recommend setting up a virtual environment with venv:

python3 -m venv env

After that, enter the virtual environment with

source env/bin/activate

and install the dependencies in the requirements.txt file with:

python3 -m pip install -r requirements.txt

Experiments

Training a model

To reproduce the experiments in the paper with "Meta SVDD" and "One-class Prototypical Network", first we must train a model for each. To do that, inside the virtual enviroment, run

python3 -m meta_occ.train data/ --dataset $dataset --method $method

and replace $dataset with either omniglot or cifar_fs, and $method with either meta_svdd or protonet. The trained model will be saved in a file called model.pth.

The full list of possible arguments for meta_occ.train is

usage: train.py [-h]
                [--dataset {omniglot,miniimagenet,tieredimagenet,cifar_fs}]
                [--method {protonet,meta_svdd}] [--shot SHOT]
                [--batch_size BATCH_SIZE] [--query_size QUERY_SIZE]
                [--val_episodes VAL_EPISODES] [--learning_rate LEARNING_RATE]
                [--patience PATIENCE] [--save_path SAVE_PATH] dataset_folder

positional arguments:
  dataset_folder        Path to the dataset folder.

optional arguments:
  -h, --help            show this help message and exit
  --dataset {omniglot,miniimagenet,tieredimagenet,cifar_fs}
                        Dataset in which to train the model (Default:
                        "omniglot").
  --method {protonet,meta_svdd}
                        Meta one-class classification method (Default:
                        "meta_svdd").
  --shot SHOT           Number of examples in the support set (Default: 5).
  --batch_size BATCH_SIZE
                        Number of episodes in a batch (Default: 16).
  --query_size QUERY_SIZE
                        Number of examples in each query set (Default: 10).
  --val_episodes VAL_EPISODES
                        Number of validation episodes (Default: 500).
  --learning_rate LEARNING_RATE
                        Learning rate (Default: 5e-4)
  --patience PATIENCE   Early stopping patience (Default: 10).
  --save_path SAVE_PATH
                        Path in which to save model (Default: "model.pth").

AUC experiment

To reproduce the results in the last column of table 1 in the paper, run:

python3 -m meta_occ.evaluate data/ --dataset $dataset --method meta_svdd --model_path $model --metric auc

and replace $dataset with either omniglot or cifar_fs, and $model_path with the path to a meta_svdd model file trained in the appropriate dataset.

Shallow baseline

To reproduce the results in the first column of table 2 in the paper (i.e. the results for the One-class SVM with PCA), run

python3 -m meta_occ.baseline_grid_search data/ --dataset $dataset

Citing

If you find the code in this repository useful in your research, please consider citing:

@article{dahia2021meta,
    title={Meta Learning for Few-Shot One-class Classification},
    author={Gabriel Dahia and Maurício Pamplona Segundo},
    year = {2021},
    journal = {AI},
    issn = {2673-2688},
    doi = {10.3390/ai2020012}
}