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SUR: Selecting from Universal Representations

This repository contains the code to reproduce the few-shot classification experiment on MetaDataset carried out in Selecting Relevant Features from a Universal Representation for Few-shot Learning.

Dependencies

This code requires the following:

  • Python 3.6 or greater
  • PyTorch 1.0 or greater
  • TensorFlow 1.14 or greater

Installation

  1. Clone or download this repository.
  2. Configure Meta-Dataset:

    • Follow the the "User instructions" in the Meta-Dataset repository (https://github.com/google-research/meta-dataset) for "Installation" and "Downloading and converting datasets". Brace yourself, the full process would take around a day.

      NOTE: the MetaDataset codebase has significantly changed from the release of this code and it won't work as is. Please, run git checkout 056ccac in the MetaDataset root folder to checkout to the code's version used with in this project.

    • If you want to test out-of-domain behavior on additional datasets, namely, MNIST, CIFAR10, CIFAR100, follow the installation instructions in the CNAPs repository to get these datasets. This step is takes little time and we recommended to do it.

Usage

Here is how to initialize, train and test our method:

Initialization

  1. Before doing anything, first run the following commands.

    ulimit -n 50000 export META_DATASET_ROOT=<root directory of the cloned or downloaded Meta-Dataset repository> export RECORDS=<the directory where tf-records of MetaDataset are stored>

    Note the above commands need to be run every time you open a new command shell.

  2. Enter the root directory of this project, i.e. the directory where this project was cloned or downloaded.

Getting the Feature Extractors

  1. The easiest way is to download our pre-trained models and use them to obtain a universal set of features directly. If that is what you want, execute the following command in the root directory of this project:

    wget http://thoth.inrialpes.fr/research/SUR/all_weights.zip && unzip all_weights.zip && rm all_weights.zip

It will donwnload all the weights and place them in the ./weights directory.

  1. Alternatively, instead of using the pretrained models, one can train the models from scratch. To train 8 independent feature extractors, run:

./scripts/train_networks.sh

And/or to train a parametric network family, run:

./scripts/train_pnf.sh

Testing

  1. This step would run our SUR procedure to select appropriate features from a universal feature set. To select from features obtained with different networks, run:

python test.py --model.backbone=resnet18

To select from features obtained with a parametric network family, run:

python test.py --model.backbone=resnet18_pnf

Note: If you train the models yourself, be sure you have trained the corresponding extractors.

Offline Testing (optional)

To speed up the testing procedure, one could first dump the features on the hard drive, and then use them for selection directly, without needing to run a CNN. To do so, follow the steps:

  1. Dump test features extracted from the test episodes on your hard drive by running

./scripts/dump_test_episodes.sh

  1. Test SUR offline. Depending on your desired feature extractor, run:

python test_offline.py --model.backbone=resnet18 or python test_offline.py --model.backbone=resnet18_pnf

This step is useful for those who want to experiment with selection by SUR and want to avoid recomputing the same features every run.

Expected Results

Below are the results extracted from our papers. The results will vary from run to run by a percent or two up or down due to the fact that the Meta-Dataset reader generates different tasks each run, due randomnes in training the networks and in SUR optimization. The SUR method selects from 8 independently trained feature extractors, while SUR-pnf selectrs from outputs of a parametric network family, which has fewer parameters. More details about that could be found in the original paper.

Models trained on all datasets

Dataset SUR SUR-pnf
Imagenet 56.1±1.1 56.0±1.1
Omniglot 93.1±0.5 90.0±0.6
Aircraft 84.6±0.7 79.7±0.8
Birds 70.6±1.0 75.9±0.9
Textures 71.0±0.8 72.5±0.7
Quick Draw 81.3±0.6 76.7±0.7
Fungi 64.2±1.1 49.8±1.1
VGG Flower 82.8±0.8 90.0±0.6
Traffic Signs 53.4±1.0 52.2±0.8
MSCOCO 50.1±1.0 50.2±1.0
MNIST 94.5±0.5 93.1±0.4
CIFAR10 64.1±1.0 65.9±0.8
CIFAR100 56.1±1.0 57.1±1.0

Citation

If you use this code, please cite our Selecting Relevant Features from a Universal Representation for Few-shot Learning paper:

@article{dvornik2020selecting,
  title={Selecting Relevant Features from a Universal Representation for Few-shot Classification},
  author={Dvornik, Nikita and Schmid, Cordelia and Mairal, Julien},
  journal={arXiv preprint arXiv:2003.09338},
  year={2020}
}