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TensorFlow implementation of R2D2 from "Meta-learning with differentiable closed-form solvers" (ICLR 2019)
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Meta-learning with differentiable closed-form solvers

This repo implements the paper "Meta-learning with differentiable closed-form solvers" [1] in TensorFlow. It builds on code from MAML (link) [2].

A paper with a reproducible explanation of implementation details of this code can be found here (oral presentation @ ICLR 2019 RML).

Dependencies

This code requires the following:

  • python 2.* or python 3.*
  • TensorFlow v1.0+

Data

For the Omniglot, MiniImagenet and CIFAR-FS data, see the usage instructions in data/omniglot_resized/resize_images.py and data/miniImagenet/proc_images.py and data/CIFARFS/get_cifarfs.py respectively.

Usage

To run the code, see the usage instructions at the top of main.py.

Results (updated)

After 60,000 (60k) iterations, with a 95% confidence interval:

Dataset, method this code
MAML model
accuracy
this code
R2D2 model
accuracy
reported by
R2D2 [2]
CIFAR-FS, R2D2 5-way, 1-shot 57.0 ± 1.7% 62.7 ± 1.8% 65.3 ± 0.2%
CIFAR-FS, R2D2 5-way, 5-shot 68.9 ± 0.9% 75.1 ± 0.9% 79.4 ± 0.1%
CIFAR-FS, R2D2 2-way, 1-shot 82.4 ± 2.6% 87.3 ± 2.3% 83.4 ± 0.3%
CIFAR-FS, R2D2 2-way, 5-shot 88.0 ± 1.1% 90.7 ± 1.0% 91.1 ± 0.2%
miniImagenet, R2D2 5-way, 1-shot 48.1 ± 1.8% 51.7 ± 1.8% 51.5 ± 0.2%
miniImagenet, R2D2 5-way, 5-shot 63.1 ± 0.9% 66.2 ± 0.9% 68.8 ± 0.2%
miniImagenet, R2D2 2-way, 1-shot 77.3 ± 2.8% 79.5 ± 2.6% 76.7 ± 0.3%
miniImagenet, R2D2 2-way, 5-shot 85.4 ± 1.1% 87.3 ± 1.1% 86.8 ± 0.2%

R2D2 results

Cite this work

If you use (part of) this code or work, please cite the following work:

@article{devosreproducing,
  title={Reproducing Meta-learning with differentiable closed-form solvers},
  author={Devos, Arnout and Chatel, Sylvain and Grossglauser, Matthias},
  booktitle={International Conference on Learning Representations},
  year={2019},
  url={https://openreview.net/forum?id=BJx0N2I6IN},
}

References

[1] Bertinetto, Luca, et al. "Meta-learning with differentiable closed-form solvers." arXiv preprint arXiv:1805.08136 (2018).

[2] Finn, Chelsea, Pieter Abbeel, and Sergey Levine. "Model-agnostic meta-learning for fast adaptation of deep networks." arXiv preprint arXiv:1703.03400 (2017). 7

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