MetaVRF

This repository contains source code of the ICML 2020 paper:(Learning to Learn Kernels with Variational Random Features) https://arxiv.org/abs/2006.06707

Learning to Learn Kernels with Variational Random Features

The main components of the repository are:

• run_classifier.py: script to run classification experiments on Omniglot and miniImageNet
• features.py: deep neural networks for feature extraction and image generation
• inference.py: amortized inference networks for various versions
• utilities.py: assorted functions to support the repository
• train_regression.py: script to run regression experiments.

Dependencies

This code requires the following:

• python 3
• TensorFlow v1.0+

Data

For Omniglot, miniImagenet, see the usage instructions in data/save_omniglot_data.py, data/save_mini_imagenet_data.py, respectively.

Usage

• To run few-shot classification, see the usage instructions at the top of run_classifier.py.
• To run view regression, see the usage instructions at the top of train_regression.py and test_regression.py.

Extending the Model

There are a number of ways the repository can be extended:

• Data: to use alternative datasets, a class must be implemented to handle the new dataset. The necessary methods for the class are: __init__, get_batch, get_image_height, get_image_width, and get_image_channels. For example signatures see omniglot.py, mini_imagenet.py or omniglot.py. Note that the code currently handles only image data. Finally, add the initialization of the class to the file data.py.

• Feature extractors: to use alternative feature extractors, simply implement a desired feature extractor in features.py and change the function call in run_classifier.py. For the required signature of a feature extractor see the function extract_features in features.py.

Citation

If MetaVRF is used in your paper/experiments, please cite the following paper.

@misc{zhen2020learning,
    title={Learning to Learn Kernels with Variational Random Features},
    author={Xiantong Zhen and Haoliang Sun and Yingjun Du and Jun Xu and Yilong Yin and Ling Shao and Cees Snoek},
    year={2020},
    eprint={2006.06707},
    archivePrefix={arXiv},
    primaryClass={cs.LG}}