https://github.com/dragen1860/cactu
https://github.com/dragen1860/cactu
Last synced: 7 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/dragen1860/cactu
- Owner: dragen1860
- License: mit
- Created: 2018-10-15T06:32:34.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:57:59.000Z (almost 3 years ago)
- Last Synced: 2025-02-13T23:29:26.805Z (8 months ago)
- Language: Python
- Size: 55.7 KB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CACTUs-MAML
Clustering to Automatically Generate Tasks for Unsupervised Model-Agnostic Meta-Learning.
This code was used to produce the CACTUs-MAML results and baselines in the paper [Unsupervised Learning via Meta-Learning](https://arxiv.org/abs/1810.02334).
This repository was built off of [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://github.com/cbfinn/maml).
### Dependencies
The code was tested with the following setup:
* Ubuntu 16.04
* Python 3.5.2
* Tensorflow-GPU 1.10
You can set up a Python virtualenv, activate it, and install the dependencies like so:
```
virtualenv venv --python=/usr/bin/python3
source venv/bin/activate
pip install -r requirements.txt
```
### Data
The Omniglot splits with ACAI and BiGAN encodings used for the results in the paper are available [here](https://drive.google.com/open?id=1i6kEbySnR51jT3pW_60E3PGkIOKmxTfQ).
Download and extract the archive's contents into this directory.
Unfortunately, due to licensing issues, I am not at liberty to re-distribute the miniImageNet or CelebA datasets. The code for these datasets is still presented for posterity.
### Usage
You can find examples of scripts in ```/scripts```. Metrics can be visualized using Tensorboard. Evaluation results are saved to a .csv file in a run's log folder. All results were obtained using a single GPU.
### Credits
The unsupervised representations were computed using four open-source codebases from prior works.
* [Adversarial Feature Learning](https://github.com/jeffdonahue/bigan)
* [Deep Clustering for Unsupervised Learning of Visual Features](https://github.com/facebookresearch/deepcluster)
* [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://github.com/openai/InfoGAN)
* [Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer](https://github.com/brain-research/acai)
### Contact
To ask questions or report issues, please open an issue on the [issues tracker](https://github.com/hsukyle/cactus-maml/issues).